Research ArticleModel for Vaccine Design by Prediction of B-Epitopes ofIEDB Given Perturbations in Peptide Sequence In Vivo ProcessExperimental Techniques and Source or Host Organisms
Humberto Gonzaacutelez-Diacuteaz12 Laacutezaro G Peacuterez-Montoto3 and Florencio M Ubeira3
1 Department of Organic Chemistry II University of the Basque Country UPVEHU 48940 Bilbao Spain2 Ikerbasque Basque Foundation for Science 48011 Bilbao Spain3 Department of Microbiology and Parasitology University of Santiago de Compostela (USC) 15782 Santiago de Compostela Spain
Correspondence should be addressed to Humberto Gonzalez-Dıaz humbertogonzalezdiazehues
Received 21 August 2013 Accepted 17 November 2013 Published 12 January 2014
Academic Editor Darren R Flower
Copyright copy 2014 Humberto Gonzalez-Dıaz et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited
Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a relatedproblemwithout known exact solutionOne problemof this type in immunology is the prediction of the possible action of epitope ofone peptide after a perturbation or variation in the structure of a known peptide andor other boundary conditions (host organismbiological process and experimental assay) However to the best of our knowledge there are no reports of general-purposeperturbation models to solve this problem In a recent work we introduced a new quantitative structure-property relationshiptheory for the study of perturbations in complex biomolecular systems In this work we developed the first model able to classifymore than 200000 cases of perturbations with accuracy sensitivity and specificity gt90 both in training and validation seriesThe perturbations include structural changes in gt50000 peptides determined in experimental assays with boundary conditionsinvolving gt500 source organisms gt50 host organisms gt10 biological process and gt30 experimental techniques The model maybe useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design
1 Introduction
National Institute of Allergy and InfectiousDiseases (NIAID)supported the launch in 2004 of the Immune Epitope Data-base (IEDB) httpwwwiedborg [1ndash4] The IEDB systemwithdrew information from approximately 99 of all paperspublished to date that describe immune epitopes In doingso IEDB system analyses over 22 million PubMed abstractsand subsequently curated asymp13 K references including asymp7Kmanuscripts about infectious diseasesasymp1 K about allergy top-ics asymp4K about autoimmunity and 1 K about transplantallo-antigen topics [5] IEDB lists a huge amount of informationabout the molecular structure as well as the experimentalconditions (119888
119894119895) in which different 119894th molecules were deter-
mined to be immune epitopes or notThis explosion of infor-mation makes necessary both querydisplay functions forretrieval of known data from IEDB as well predictive tools for
new epitopes Salimi et al [5] reviewed advances in epitopeanalysis and predictive tools available in the IEDB In factIEDB analysis resource (IEDB-AR httptoolsiedborg) is acollection of tools for prediction of molecular targets of T-and B-cell immune responses (ie epitopes) [6 7]
On the other hand Quantitative Structure-ActivityProp-erty Relationships (QSARQSPR) techniques are useful toolto predict newdrugs RNA drug-protein complexes and pro-tein-protein complexes In general QSARQSPR-like meth-ods transform molecular structures into numeric moleculardescriptors (120582
119894) in a first stage and later fit a model to pre-
dict the biological process For example DRAGON [8ndash10]CODESSA [11 12]MOE [13] TOPS-MODE [14ndash17] TOMO-COMD [18 19] and MARCH-INSIDE [20] are among themost used softwares to calculate molecular descriptors basedon quantum mechanics (QM) andor graph theory [21ndash27]The software STATISTICA [28] and WEKA [29] are often
Hindawi Publishing CorporationJournal of Immunology ResearchVolume 2014 Article ID 768515 15 pageshttpdxdoiorg1011552014768515
2 Journal of Immunology Research
used to performmultivariate statistics andor machine learn-ing (ML) analysis in order to preprocess data and later fitthe final QSARQSPR model using techniques like princi-pal component analysis (PCA) linear discriminant analysis(LDA) support vector machine (SVM) or artificial neuralnetworks (ANN) [28]
QSARQSPRmodels are also important in immunoinfor-matics to predict the propensity of different molecular struc-tures to play different roles in immunological processesTheyinclude skin vaccine adjuvants and sensitizers [30ndash38] drugsand their activitytoxicity protein targets in the immunesystem [39] and epitopes [40ndash49] Moreover Reche andReinherz [50] implemented PEPVAC (promiscuous epitope-based vaccine) a web server for the formulation of multi-epitope vaccines that predict peptides binding to five distinctHLA class I supertypes (A2 A3 B7 A24 and B15) PEPVACcan also identify conserved MHC ligands as well as thosewith a C-terminus resulting from proteasomal cleavage TheDana-Farber Cancer Institute hosted the PEPVAC server atthe site httpimmunaxdfciharvardeduPEPVAC To closewith a last example Lafuente and Reche [51] reviewed theavailable methods for predicting MHC-peptide binding anddiscussed their most relevant advantages and drawbacks
In many complex QSPR-like problems in immunoinfor-matics like in other areas we know the exact experimentalresult (known solution) of the problem but we are interestedin the possible result obtained after a change (perturbation)on one or multiple values of the initial conditions of theexperiment (new solution) For instance we often know forlarge collections of 119894th molecules (119898
119894) organic compounds
drugs xenobiotics andor peptide sequences the efficiencyof the compound 120576(119888
119894119895) as adjuvant action as epitope immun-
otoxicity andor the interaction (affinity inhibition etc) withimmunological targets In addition we often known for eachmolecule the exact conditions (119888
119894119895) of assay for the initial
experiment including structure of the molecule 119898119894(drug
adjuvant and sequence of the peptide) source organism (so)host organism (ho) immunological process (ip) experimen-tal technique (tq) concentration temperature time solventsand coadjuvants This is the case of big data retrieved fromvery large databases like IEDB [1ndash4] and CHEMBL [52]However we do not know the possible result of the experi-ment if we change at least one of these conditions (perturba-tion) We refer to small changes or perturbations in bothstructure and condition for input or output variables Itmeans that we include changes in ho so ip and tq changes ofthe compound by one analogue compound with similarstructure changes in the sequence of the epitope (artificial byorganic synthesis or natural mutations) and polarity of thesolvent or coadjuvants In these cases we could use a per-turbation theory model to solve the QSARQSPR problemPerturbation theory includes methods that add ldquosmallrdquo termsto a known solution of a problem in order to approach asolution to a related problemwithout known solution Pertur-bation models have been widely used in all branches ofscience from QM to astronomy and life sciences includingchaos or ldquobutterfly effectrdquo Bohrrsquos atomic theory Heisenbergrsquosmechanics Zeemanrsquos and Starkrsquos effects and other models
with applications in like protein spectroscopy and others [53ndash57] In a very recent work Gonzalez-Diaz et al [58] formu-lated a general-purpose perturbation theory or model formultiple-boundaryQSPRQSARproblems However there isnot report in the immunoinformatics literature of a generalQSPR perturbation model for IEDB B-epitopes Here wereport the first example of QSPR-perturbation model for B-epitopes reported in IEDB able to predict the probability ofoccurrence of an epitope after a perturbation in the sequencethe experimental technique the exposition process andorthe source or host organisms
2 Materials and Methods
21 Molecular Descriptors for Peptides We calculated themolecular descriptors of the structure of peptides using thesoftwareMARCH-INSIDE (MI) based on the algorithmwiththe same name [59] The MI approach uses a Markov Chainmethod to calculate the 119896th mean values of different physico-chemical molecular properties 120582(119898
119894) for 119894th molecules (119898)
These 120582(119898119894) values are calculated as an average of 119896120582(119898119894)
values for all atoms placed at topological distance 119889 le 119896which are in turn the means of atomic properties (120582
119895) for all
atoms in the molecule and its neighbors placed at 119889 = 119896For instance it is possible to derive average estimationsof molecular refractivities 119896MR(119898
119894) partition coefficients
119896119875(119898119894) and hardness 119896120578(119898
119894) for atoms placed at different
topological distances 119889 le 119896 In this first work we calculatedonly one type of 120582(119898
119894) values We calculated for all peptides
the average value 120594(119898119894) of all the atomic electronegativities 120594
119894
for all 120575119894atoms connected to the 119894th atom (119894 rarr 119895) and their
neighbors placed at a distance 119889 le 5 [59]
120594 (119898119894) =
1
6
5
sum
119896=0
119896120594119895=
1
6
5
sum
119896=0
120575119894
sum
119894rarr 119895
119901119896(120594119895) sdot 120594119895 (1)
We calculate the probabilities 119896119901(120582119895) for any atomic prop-
erty including 119896119901(120594119895) using a Markov Chain model for the
gradual effects of the neighboring atoms at different distancesin the molecular backbone This method has been explainedin detail in many previous works so we omit the details here[59]
22 Electronegativity Perturbation Model for Prediction of B-Epitopes Very recently Gonzalez-Diaz et al [58] formulateda general-purpose perturbation theory ormodel formultiple-boundary QSPRQSAR problems We adapted here this newtheory or modeling method to approach to the peptide pre-diction problem from the point of view of perturbation the-ory Let be a set of 119894th peptide molecules denoted as119898
119894with a
value of efficiency 120576119894119895as epitopes experimentally determined
under a set of boundary conditions 119888119895equiv (1198880 1198881 1198882 1198883 119888
119899)
We put the main emphasis here on peptides reported in thedatabase IEDB In this sense the boundary conditions 119888
119895used
here are the same reported in this database 1198880= is the specific
Journal of Immunology Research 3
peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888
4= tq119895 In general
so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576
119894119895 Consequently we have to predict the
discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-
opes reported in the conditions 119888119895and 120582(120576
119894119895) = 0 otherwise
Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576
119894119895) = 120582(120576
119894119895)ref minus 120582(120576119894119895)new that takes
place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of
conditions 119888119895for initial (reference) and final processes (new)
Consequently to predict Δ120582(120576119894119895) we have to predict only
120582(120576119894119895)new the efficiency function of the new state obtained by
a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881
119894119895as the state information function for the reference
and new states According to our recent model [58] we canwrite 119881
119894119895as a function of the conditions and structure of the
peptide 119898119894as follows In fact the variational state functions
119881119894119895have to be written in pairs in order to describe the initial
(reference) and final (new) states of a perturbation as follow
119881119894119895= 120582(120576
119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)
119881119902119903= 120582(120576
119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)
(2)
The state function 119899119881119894119895is for the 119894th peptide measured
under a set of 119888119894119895boundary conditions in output final or new
stateThe conjugated state function 119903119881119902119903is for the 119902th peptide
measured under a set of 119888119902119903boundary conditions for the input
initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider
Δ119881 = 119881119894119895minus 119881119902119903= [
[
120582(120576119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)]
]
minus [120582(120576119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)]
(3)
Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H
0 existence of
one small and constant value of the perturbation functionΔ119881 = 119890
0for all the pairs of peptides and a linear relationship
Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes
Data Stat Pred Predicted epitope perturbationssubset param 120582(120576
119894119895) = 1 120582(120576
119894119895) = 0
120582(120576119894119895) = 1 Sp 970 84607 2660
120582(120576119894119895) = 0 Sn 936 4354 63548
Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840
120582(120576119894119895) = 0 Sn 933 1485 20641
Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel
between perturbations of inputoutput boundary conditionswith coefficients 119886
119894119895 119887119894119895 119888119902119903 and 119889
119894119895 Consider
1198900= Δ119881
= [
[
119886119894119895sdot 120582(120576119894119895)new
minus
4
sum
119895=1
119887119894119895sdot (120582 (119898
119894) minus 120582(119888
119895)avg)]
]
minus [119888119902119903sdot 120582(120576119902119903)refminus
4
sum
119903=1
119889119902119903sdot (120582 (119898
119902) minus 120582(119888
119903)avg)]
(4)
We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576
119894119895)new In this case considering 119887
119894119895asymp 119889119902119903 we can
obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider
120582(120576119894119895)new
= (
119888119902119903
119886119894119895
) sdot 120582(120576119902119903)ref
+ [
[
4
sum
119895=1
(
119887119902119903
119886119894119895
) sdot (120582 (119898119894) minus 120582(119888
119895)avg)
new]
]
minus [
4
sum
119903=1
(
119889119902119903
119886119894119895
) sdot (120582 (119898119902) minus 120582(119888
119903)avg)ref
]
+ (
1198900
119886119894119895
)
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref
+
4
sum
119895=1
1015840119889119894119895sdot Δ (120582 (119898
119894) minus 120582(119888
119895)avg) +10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120582
119894119895119902119903+10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900
(5)
4 Journal of Immunology Research
Table 2 Average values and count of input-output cases for differentorganisms process and techniques
Source organism (so) 119873in 119873outlowast120594
Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out
lowast120594
Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790
Journal of Immunology Research 5
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out
lowast120594
AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Technique (tq) 119873in 119873outlowast120594
ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition
3 Results and Discussion
We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was
120582(120576119894119895)new
= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq
+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so
minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149
119873 = 155169 119877119888 = 092
119880 = 015 119901 lt 001
(6)
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
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Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
2 Journal of Immunology Research
used to performmultivariate statistics andor machine learn-ing (ML) analysis in order to preprocess data and later fitthe final QSARQSPR model using techniques like princi-pal component analysis (PCA) linear discriminant analysis(LDA) support vector machine (SVM) or artificial neuralnetworks (ANN) [28]
QSARQSPRmodels are also important in immunoinfor-matics to predict the propensity of different molecular struc-tures to play different roles in immunological processesTheyinclude skin vaccine adjuvants and sensitizers [30ndash38] drugsand their activitytoxicity protein targets in the immunesystem [39] and epitopes [40ndash49] Moreover Reche andReinherz [50] implemented PEPVAC (promiscuous epitope-based vaccine) a web server for the formulation of multi-epitope vaccines that predict peptides binding to five distinctHLA class I supertypes (A2 A3 B7 A24 and B15) PEPVACcan also identify conserved MHC ligands as well as thosewith a C-terminus resulting from proteasomal cleavage TheDana-Farber Cancer Institute hosted the PEPVAC server atthe site httpimmunaxdfciharvardeduPEPVAC To closewith a last example Lafuente and Reche [51] reviewed theavailable methods for predicting MHC-peptide binding anddiscussed their most relevant advantages and drawbacks
In many complex QSPR-like problems in immunoinfor-matics like in other areas we know the exact experimentalresult (known solution) of the problem but we are interestedin the possible result obtained after a change (perturbation)on one or multiple values of the initial conditions of theexperiment (new solution) For instance we often know forlarge collections of 119894th molecules (119898
119894) organic compounds
drugs xenobiotics andor peptide sequences the efficiencyof the compound 120576(119888
119894119895) as adjuvant action as epitope immun-
otoxicity andor the interaction (affinity inhibition etc) withimmunological targets In addition we often known for eachmolecule the exact conditions (119888
119894119895) of assay for the initial
experiment including structure of the molecule 119898119894(drug
adjuvant and sequence of the peptide) source organism (so)host organism (ho) immunological process (ip) experimen-tal technique (tq) concentration temperature time solventsand coadjuvants This is the case of big data retrieved fromvery large databases like IEDB [1ndash4] and CHEMBL [52]However we do not know the possible result of the experi-ment if we change at least one of these conditions (perturba-tion) We refer to small changes or perturbations in bothstructure and condition for input or output variables Itmeans that we include changes in ho so ip and tq changes ofthe compound by one analogue compound with similarstructure changes in the sequence of the epitope (artificial byorganic synthesis or natural mutations) and polarity of thesolvent or coadjuvants In these cases we could use a per-turbation theory model to solve the QSARQSPR problemPerturbation theory includes methods that add ldquosmallrdquo termsto a known solution of a problem in order to approach asolution to a related problemwithout known solution Pertur-bation models have been widely used in all branches ofscience from QM to astronomy and life sciences includingchaos or ldquobutterfly effectrdquo Bohrrsquos atomic theory Heisenbergrsquosmechanics Zeemanrsquos and Starkrsquos effects and other models
with applications in like protein spectroscopy and others [53ndash57] In a very recent work Gonzalez-Diaz et al [58] formu-lated a general-purpose perturbation theory or model formultiple-boundaryQSPRQSARproblems However there isnot report in the immunoinformatics literature of a generalQSPR perturbation model for IEDB B-epitopes Here wereport the first example of QSPR-perturbation model for B-epitopes reported in IEDB able to predict the probability ofoccurrence of an epitope after a perturbation in the sequencethe experimental technique the exposition process andorthe source or host organisms
2 Materials and Methods
21 Molecular Descriptors for Peptides We calculated themolecular descriptors of the structure of peptides using thesoftwareMARCH-INSIDE (MI) based on the algorithmwiththe same name [59] The MI approach uses a Markov Chainmethod to calculate the 119896th mean values of different physico-chemical molecular properties 120582(119898
119894) for 119894th molecules (119898)
These 120582(119898119894) values are calculated as an average of 119896120582(119898119894)
values for all atoms placed at topological distance 119889 le 119896which are in turn the means of atomic properties (120582
119895) for all
atoms in the molecule and its neighbors placed at 119889 = 119896For instance it is possible to derive average estimationsof molecular refractivities 119896MR(119898
119894) partition coefficients
119896119875(119898119894) and hardness 119896120578(119898
119894) for atoms placed at different
topological distances 119889 le 119896 In this first work we calculatedonly one type of 120582(119898
119894) values We calculated for all peptides
the average value 120594(119898119894) of all the atomic electronegativities 120594
119894
for all 120575119894atoms connected to the 119894th atom (119894 rarr 119895) and their
neighbors placed at a distance 119889 le 5 [59]
120594 (119898119894) =
1
6
5
sum
119896=0
119896120594119895=
1
6
5
sum
119896=0
120575119894
sum
119894rarr 119895
119901119896(120594119895) sdot 120594119895 (1)
We calculate the probabilities 119896119901(120582119895) for any atomic prop-
erty including 119896119901(120594119895) using a Markov Chain model for the
gradual effects of the neighboring atoms at different distancesin the molecular backbone This method has been explainedin detail in many previous works so we omit the details here[59]
22 Electronegativity Perturbation Model for Prediction of B-Epitopes Very recently Gonzalez-Diaz et al [58] formulateda general-purpose perturbation theory ormodel formultiple-boundary QSPRQSAR problems We adapted here this newtheory or modeling method to approach to the peptide pre-diction problem from the point of view of perturbation the-ory Let be a set of 119894th peptide molecules denoted as119898
119894with a
value of efficiency 120576119894119895as epitopes experimentally determined
under a set of boundary conditions 119888119895equiv (1198880 1198881 1198882 1198883 119888
119899)
We put the main emphasis here on peptides reported in thedatabase IEDB In this sense the boundary conditions 119888
119895used
here are the same reported in this database 1198880= is the specific
Journal of Immunology Research 3
peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888
4= tq119895 In general
so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576
119894119895 Consequently we have to predict the
discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-
opes reported in the conditions 119888119895and 120582(120576
119894119895) = 0 otherwise
Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576
119894119895) = 120582(120576
119894119895)ref minus 120582(120576119894119895)new that takes
place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of
conditions 119888119895for initial (reference) and final processes (new)
Consequently to predict Δ120582(120576119894119895) we have to predict only
120582(120576119894119895)new the efficiency function of the new state obtained by
a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881
119894119895as the state information function for the reference
and new states According to our recent model [58] we canwrite 119881
119894119895as a function of the conditions and structure of the
peptide 119898119894as follows In fact the variational state functions
119881119894119895have to be written in pairs in order to describe the initial
(reference) and final (new) states of a perturbation as follow
119881119894119895= 120582(120576
119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)
119881119902119903= 120582(120576
119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)
(2)
The state function 119899119881119894119895is for the 119894th peptide measured
under a set of 119888119894119895boundary conditions in output final or new
stateThe conjugated state function 119903119881119902119903is for the 119902th peptide
measured under a set of 119888119902119903boundary conditions for the input
initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider
Δ119881 = 119881119894119895minus 119881119902119903= [
[
120582(120576119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)]
]
minus [120582(120576119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)]
(3)
Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H
0 existence of
one small and constant value of the perturbation functionΔ119881 = 119890
0for all the pairs of peptides and a linear relationship
Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes
Data Stat Pred Predicted epitope perturbationssubset param 120582(120576
119894119895) = 1 120582(120576
119894119895) = 0
120582(120576119894119895) = 1 Sp 970 84607 2660
120582(120576119894119895) = 0 Sn 936 4354 63548
Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840
120582(120576119894119895) = 0 Sn 933 1485 20641
Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel
between perturbations of inputoutput boundary conditionswith coefficients 119886
119894119895 119887119894119895 119888119902119903 and 119889
119894119895 Consider
1198900= Δ119881
= [
[
119886119894119895sdot 120582(120576119894119895)new
minus
4
sum
119895=1
119887119894119895sdot (120582 (119898
119894) minus 120582(119888
119895)avg)]
]
minus [119888119902119903sdot 120582(120576119902119903)refminus
4
sum
119903=1
119889119902119903sdot (120582 (119898
119902) minus 120582(119888
119903)avg)]
(4)
We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576
119894119895)new In this case considering 119887
119894119895asymp 119889119902119903 we can
obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider
120582(120576119894119895)new
= (
119888119902119903
119886119894119895
) sdot 120582(120576119902119903)ref
+ [
[
4
sum
119895=1
(
119887119902119903
119886119894119895
) sdot (120582 (119898119894) minus 120582(119888
119895)avg)
new]
]
minus [
4
sum
119903=1
(
119889119902119903
119886119894119895
) sdot (120582 (119898119902) minus 120582(119888
119903)avg)ref
]
+ (
1198900
119886119894119895
)
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref
+
4
sum
119895=1
1015840119889119894119895sdot Δ (120582 (119898
119894) minus 120582(119888
119895)avg) +10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120582
119894119895119902119903+10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900
(5)
4 Journal of Immunology Research
Table 2 Average values and count of input-output cases for differentorganisms process and techniques
Source organism (so) 119873in 119873outlowast120594
Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out
lowast120594
Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790
Journal of Immunology Research 5
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out
lowast120594
AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Technique (tq) 119873in 119873outlowast120594
ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition
3 Results and Discussion
We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was
120582(120576119894119895)new
= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq
+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so
minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149
119873 = 155169 119877119888 = 092
119880 = 015 119901 lt 001
(6)
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Disease Markers
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BioMed Research International
OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
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Computational and Mathematical Methods in Medicine
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Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 3
peptide 1198881= so119895 1198882= ho119895 1198883= ip119895 and 119888
4= tq119895 In general
so is the organism that expresses the peptide (but it caninclude also artificial peptides cellular lines etc) ho is thehost organism exposed to the peptide by means of the bpdetected with tq As our analysis based on the data reportedby IEDB we are unable to work with continuous values ofepitope activity 120576
119894119895 Consequently we have to predict the
discrete function of B-epitope efficiency 120582(120576119894119895) = 1 for epit-
opes reported in the conditions 119888119895and 120582(120576
119894119895) = 0 otherwise
Ourmain aim is to predict the shift or change in a function ofthe output efficiency Δ120582(120576
119894119895) = 120582(120576
119894119895)ref minus 120582(120576119894119895)new that takes
place after a change variation or perturbation (Δ119881) in thestructure andor boundary conditions of a peptide of refer-ence But we know the efficiency of the process of reference120582(120576119894119895)ref in addition to the molecular structure and the set of
conditions 119888119895for initial (reference) and final processes (new)
Consequently to predict Δ120582(120576119894119895) we have to predict only
120582(120576119894119895)new the efficiency function of the new state obtained by
a change in the structure of the peptide andor the boundaryconditions Let Δ119881 be a perturbation in a function 120582 we candefine 119881
119894119895as the state information function for the reference
and new states According to our recent model [58] we canwrite 119881
119894119895as a function of the conditions and structure of the
peptide 119898119894as follows In fact the variational state functions
119881119894119895have to be written in pairs in order to describe the initial
(reference) and final (new) states of a perturbation as follow
119881119894119895= 120582(120576
119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)
119881119902119903= 120582(120576
119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)
(2)
The state function 119899119881119894119895is for the 119894th peptide measured
under a set of 119888119894119895boundary conditions in output final or new
stateThe conjugated state function 119903119881119902119903is for the 119902th peptide
measured under a set of 119888119902119903boundary conditions for the input
initial or reference state The difference Δ119881 between the new(output) state and the reference (input) state is the additiveperturbation [58] Consider
Δ119881 = 119881119894119895minus 119881119902119903= [
[
120582(120576119894119895)new
minus
4
sum
119895=1
(120582 (119898119894) minus 120582(119888
119894119895)avg)]
]
minus [120582(120576119902119903)refminus
4
sum
119903=1
(120582 (119898119902) minus 120582(119888
119902119903)avg)]
(3)
Equation (3) described before opens the door to testdifferent hypothesis A simple hypotheses is H
0 existence of
one small and constant value of the perturbation functionΔ119881 = 119890
0for all the pairs of peptides and a linear relationship
Table 1 Results of QSPR-perturbation model for IEDB B-Epitopes
Data Stat Pred Predicted epitope perturbationssubset param 120582(120576
119894119895) = 1 120582(120576
119894119895) = 0
120582(120576119894119895) = 1 Sp 970 84607 2660
120582(120576119894119895) = 0 Sn 936 4354 63548
Total train Ac 955120582(120576119894119895) = 1 Sp 971 28060 840
120582(120576119894119895) = 0 Sn 933 1485 20641
Total cv Ac 954Bold font is used to highlight the number of cases correctly classified by themodel
between perturbations of inputoutput boundary conditionswith coefficients 119886
119894119895 119887119894119895 119888119902119903 and 119889
119894119895 Consider
1198900= Δ119881
= [
[
119886119894119895sdot 120582(120576119894119895)new
minus
4
sum
119895=1
119887119894119895sdot (120582 (119898
119894) minus 120582(119888
119895)avg)]
]
minus [119888119902119903sdot 120582(120576119902119903)refminus
4
sum
119903=1
119889119902119903sdot (120582 (119898
119902) minus 120582(119888
119903)avg)]
(4)
We can use elemental algebraic operations to obtain fromthese equations an expression for efficiency as epitope of thepeptide 120582(120576
119894119895)new In this case considering 119887
119894119895asymp 119889119902119903 we can
obtain the different expressions the last may be very usefulto solve the QSRR problem for the large datasets formed byIEDB B-epitopes Consider
120582(120576119894119895)new
= (
119888119902119903
119886119894119895
) sdot 120582(120576119902119903)ref
+ [
[
4
sum
119895=1
(
119887119902119903
119886119894119895
) sdot (120582 (119898119894) minus 120582(119888
119895)avg)
new]
]
minus [
4
sum
119903=1
(
119889119902119903
119886119894119895
) sdot (120582 (119898119902) minus 120582(119888
119903)avg)ref
]
+ (
1198900
119886119894119895
)
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref
+
4
sum
119895=1
1015840119889119894119895sdot Δ (120582 (119898
119894) minus 120582(119888
119895)avg) +10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120582
119894119895119902119903+10158401198900
120582(120576119894119895)new
=10158401198880sdot 120582(120576119902119903)ref+
4
sum
119895=1
1015840119889119894119895sdot ΔΔ120594119894119895119902119903+10158401198900
(5)
4 Journal of Immunology Research
Table 2 Average values and count of input-output cases for differentorganisms process and techniques
Source organism (so) 119873in 119873outlowast120594
Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out
lowast120594
Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790
Journal of Immunology Research 5
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out
lowast120594
AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Technique (tq) 119873in 119873outlowast120594
ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition
3 Results and Discussion
We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was
120582(120576119894119895)new
= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq
+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so
minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149
119873 = 155169 119877119888 = 092
119880 = 015 119901 lt 001
(6)
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
4 Journal of Immunology Research
Table 2 Average values and count of input-output cases for differentorganisms process and techniques
Source organism (so) 119873in 119873outlowast120594
Homo sapiens 38920 39274 2685Plasmodium falciparum 10669 9446 2704Hepatitis C virus 9935 10239 2683Bos taurus 5671 5780 2690Canine parvovirus 5655 5637 2693Foot-mouth disease virus 4062 4176 2676Triticum aestivum 3769 3887 2703Bacillus anthracis 3602 3600 2699Human papillomavirus 3316 3414 2693Human herpesvirus 3026 3132 2684Gallus gallus 2850 2829 2689Arachis hypogaea 2648 2670 2687Mycobacterium tuberculosis 2637 2593 2688Clostridium botulinum 2588 2722 2685SARS coronavirus 2550 2704 2686Mus musculus 2334 2287 2682Hepatitis B virus 2007 2066 2680Helicobacter pylori 1958 1796 2695Hevea brasiliensis 1938 1958 2697Hepatitis E virus 1928 1941 2685Shigella flexneri 1878 1701 2699Dengue virus 2 1767 1828 2679Staphylococcus aureus 1757 1661 2694Treponema pallidum 1739 1755 2691Escherichia coli 1721 1678 2689Murine hepatitis virus 1575 1603 2692Haemophilus influenzae 1545 1587 2695Streptococcus mutans 1523 1537 2697Puumala virus (strain) 1505 1574 2689Chlamydia trachomatis 1402 1546 2704Human respiratory virus 1347 1398 2682Borrelia burgdorferi 1228 1237 2698Hepatitis delta virus 1182 1199 2690Streptococcus pyogenes 1181 1251 2697Porphyromonas gingivalis 1143 1085 2688Human enterovirus 1106 1132 2689Influenza A virus 1085 1086 2687Mycoplasma hyopneumoniae 1044 1024 2695Rattus norvegicus 1025 1039 2689Bordetella pertussis 1011 960 2685Human T-lymphotropic virus 996 1031 2680Anaplasma marginale 977 857 2707Measles virus strain 804 810 2688Fasciola hepatica 803 857 2685Neisseria meningitidis 789 853 2696Human poliovirus 766 780 2690Tityus serrulatus 764 775 2680Torpedo californica 752 788 2687
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Cryptomeria japonica 719 794 2680Mycobacterium bovis 717 733 2688Trypanosoma cruzi 691 777 2704Andes virus CHI-7913 679 687 2690Bovine papillomavirus 672 665 2692Human hepatitis 670 696 2688Leishmania infantum 659 735 2688Human parvovirus 649 691 2683Poa pratensis 648 664 2692Aspergillus fumigatus 642 709 2677Duck hepatitis 587 603 2688Olea europaea 571 577 2692Porcine reproductive 515 514 2681Fagopyrum esculentum 509 497 2685Juniperus ashei 505 568 2672Mycobacterium leprae 489 542 2690Glycine max 477 509 2685D pteronyssinus 455 464 2680Plasmodium vivax 453 446 2690Chlamydophila pneumoniae 446 462 2690Pseudomonas aeruginosa 443 454 2691Vibrio cholera 427 426 2694Streptococcus sp 426 425 2691Mycobacterium avium 425 415 2689Dermatophagoides farinae 410 390 2693Human coxsackievirus 406 392 2694Equine infectious virus 404 419 2688Babesia equi 383 371 2696Prunus dulcis 383 379 2708Human adenovirus 375 405 2686Theileria parva 366 371 2713Candida albicans 365 370 2690Porcine endogenous 355 351 2692Ovis aries 352 350 2683Chironomus thummi 347 338 2691Sus scrofa 343 362 2686Bovine leukemia virus 333 329 2676Ricinus communis 329 314 2692Androctonus australis 322 357 2685Renibacterium salmoninarum 319 350 2690Orientia tsutsugamushi 309 372 2705Anacardium occidentale 293 306 2693Conus geographus 289 295 2660Host organism (ho) 119873in 119873out
lowast120594
Homo sapiens 257293 91093 26856Mus musculus 107867 51466 26873Oryctolagus cuniculus 65053 31433 26900Bos taurus 15333 2072 26909Rattus norvegicus 9450 3562 26876Aotus sp 9044 3933 26879Sus scrofa 7725 3464 26873Gallus gallus 7507 997 26790
Journal of Immunology Research 5
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out
lowast120594
AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Technique (tq) 119873in 119873outlowast120594
ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition
3 Results and Discussion
We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was
120582(120576119894119895)new
= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq
+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so
minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149
119873 = 155169 119877119888 = 092
119880 = 015 119901 lt 001
(6)
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
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Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 5
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Canis lupus 6604 3334 26906Macaca mulatta 5261 2569 26993Ovis aries 3953 1653 26836Equus caballus 3943 2099 26842Cavia porcellus 3458 1688 26833Capra hircus 2182 1127 26830Aotus nancymaae 1659 852 26837Pan troglodytes 1614 732 26757Marmota monax 1100 509 27011Felis catus 901 279 26838Myodes glareolus 814 388 26863Anas platyrhynchos 688 342 26880Homo sapiens (human) 508 270 26851Trichosurus vulpecula 456 126 26921Mesocricetus auratus 438 104 26909Macaca cyclopis 382 193 26871O tshawytscha 333 159 26929Macaca fuscata 188 100 26667Cricetulus migratorius 171 142 27008Camelus dromedarius 171 89 26886Dicentrarchus labrax 121 55 26759Macaca fascicularis 96 52 26793Saimiri sciureus 92 44 26900Canis familiaris 77 42 26850Rattus rattus 72 31 26760Callithrix pygmaea 67 30 26920Chinchilla lanigera 41 24 26729Aotus lemurinus 30 19 26860Papio cynocephalus 27 13 27267Aotus griseimembra 26 12 27000Mustela vison 18 10 27000Chlorocebus aethiops 15 10 26875Bos indicus 13 4 26925Oncorhynchus mykiss 10 4 26700M macquariensis 9 6 26600Cricetulus griseus 8 4 26900Aotus trivirgatus 7 4 27000Process type (pt) 119873in 119873out
lowast120594
AID 111197 108536 26876OOID 32419 32617 26868OAID 19210 18954 26801OOA 15863 16303 26902NI 13430 15206 26845EWEIR 4818 4864 26843EEE 3113 3546 26906OOD 2806 2799 26887AICD 1077 1095 26812EWED 696 686 26879DEWED 280 337 26804TT 260 215 26806OOC 153 137 26800
Table 2 Continued
Source organism (so) 119873in 119873outlowast120594
Technique (tq) 119873in 119873outlowast120594
ELISA 133458 135109 26871WI 33627 33292 26887ACAbB 7780 9068 26862PhDIP 7450 4496 26771RIA 5241 5218 26858IFAIH 4454 4581 26879NIAA 4222 4316 26892FIA 2255 2276 26897PAC 1312 1219 26837IP 1127 1089 26886SPR 758 639 26860FACS 608 647 26907Other 502 495 26813SAC 484 393 26878ELISPOT 396 412 26979RDAT 366 323 26859EDAT 284 330 26800XRC 231 227 26880MS 209 179 26849PFF 171 153 26820AbDPO 162 295 26968CdC 146 205 26895IAbBA 144 183 26940IOT 124 106 26835HAGGI 115 122 26834IgMHR 89 90 26929EAAA 84 139 26922HS 82 67 26791AbdCC 73 118 26897AGG 50 60 26980CM 50 57 26863The lowastindicates that quantities like lowast120594 is the average value of the meanelectronegativity (119898119894) for all the peptides in IEDB that are epitopes for thesame boundary condition
3 Results and Discussion
We propose herein for the first time a QSRR-perturbationmodel able to predict variations in the propensity of a peptideto act as B-epitope taking into consideration the propensityof a peptide of reference and the changes in peptide sequenceimmunological process host organism source organismsand the experimental technique used The best QSPR-perturbation model found here with LDA was
120582(120576119894119895)new
= 4979 sdot 120582(120576119894119895)refminus 221642 sdot Δ120594seq
+ 8770 sdot ΔΔ120594ho + 63572 sdot ΔΔ120594so
minus 55387 sdot ΔΔ120594ip + 201919 sdot ΔΔ120594tq minus 2149
119873 = 155169 119877119888 = 092
119880 = 015 119901 lt 001
(6)
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Research and TreatmentAIDS
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
6 Journal of Immunology Research
Table3To
p100
values
ofp1
forp
ositive
perturbatio
nsin
training
serie
sNew
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
OAID
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
WI
001
0012
000
40017
0012
52124
QQQPP
Hom
osapiens
Triticum
aestivum
OOA
ELISA
52128
QQQQGG
SQSQ
KGKQ
QHom
osapiens
Glycinem
axOOA
WI
00minus0018
00002
3639
APL
GVT
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
3652
APL
TRG
SCRK
RNRS
PER
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
004
004
0039
0043
004
135959
LTRA
YAKD
VKF
GHom
osapiens
Hom
osapiens
OAID
ELISA
135963
NGQEE
KAG
VVS
TGLIGGG
Mus
musculus
MD
AID
ELISAminus004minus0038minus0047minus0033minus004
108075
PREP
QVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
108078
PTSP
SGV
EEWIV
TQVVPG
VAOryctolagus
cuniculus
Hom
osapiens
AID
ACAb
Bminus001minus000
6minus001minus0003minus0011
25113
HVVDLP
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
25126
HWGNH
SKSH
PQR
Mus
musculus
MD
AID
ELISA
002
0022
0013
0023
002
48780
PPFSPQ
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
48782
PPFT
SAV
GGVDHRS
Mus
musculus
MD
AID
SAC
002
0022
0008
0021
0021
40988
LYVVA
YQA
Mus
musculus
Viscum
album
AID
ELISA
4100
4MAARL
CCQLD
PARD
VHom
osapiens
HepatitisB
virus
OOID
ELISA
002
0018
000
60019
002
50439
QDAY
NAAG
Mus
musculus
Mycobacteriu
mscrofulaceum
AID
ELISA
5044
5QDCN
CSI
YPGHAS
GHRM
AWD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
004
0038
0028
0039
004
98849
KIPA
VFK
IDA
Hom
osapiens
Bostaurus
DEW
EDWI
98850
KKGSE
EEGDITNPIN
Hom
osapiens
Arachish
ypogaea
OOA
IFAIHminus005minus005minus0053minus004minus0051
116171
TQDQDP
BBHFF
KNIV
TPR
Hom
osapiens
Hom
osapiens
OAID
ACAb
B116
286
CGKG
LSAT
VTG
GQKG
RGSR
Oryctolagus
cuniculus
Mus
musculus
AID
MS
001
0014
0008
0017
000
9
123442
LLKD
LRKN
Hom
osapiens
Bornadiseasev
irus
EWEIR
WI
123443
LLTE
HR
MTW
DPA
QPP
RDLT
EHom
osapiens
Hom
osapiens
OOID
ELISAminus002minus002minus0025minus0017minus0022
47858
PHVVDL
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
47860
PHWIK
KPNRQ
GLG
YYS
Caprahircus
Hum
anT-lymphotropic
virus
AID
ACAb
B001
0007
0005
0013
000
9
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61792
STNPK
PQRK
TKRN
TNRR
PQD
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0025
0019
0029
003
118210
VMLY
QISEE
Hom
osapiens
Hom
osapiens
OAID
WI
118217
VTK
YITK
GWKE
VH
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0054
005
0057
004
8
130944
LFKH
SOryctolagus
cuniculus
Rattu
snorvegicus
AID
ELISA
130956
LPPR
VTP
KWSLDA
WST
WR
Hom
osapiens
MD
OOD
WI
001
000
6minus0002
0011
0012
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
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Behavioural Neurology
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 7Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDVRF
SQV
Hom
osapiens
MD
OOID
ELISA
00
00007minus0002
51199
QKK
AIE
Oryctolagus
cuniculus
Vibriocholerae
AID
ELISA
51204
QKK
NK
RNTN
RRPQ
DV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
003
0026
0018
0029
003
144783
SHVVT
MLD
NF
Hom
osapiens
Hom
osapiens
NI
ELISA
144786
SMNRG
RGTH
PSLIWM
Mus
musculus
MD
AID
ACAb
B003
0032
0023
0033
0029
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
NIAA
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
WI
00minus000
90
0
38321
LNQLA
GRM
Anas
platyrhynchos
Duckhepatitis
AID
ELISA
38323
LNQTA
RAFP
DCA
ICW
EPSP
POryctolagus
cuniculus
Bovine
leukemia
virus
AID
ACAb
Bminus001minus0008minus0022minus001minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
1446
61GRE
GYP
ADGGCA
WPA
CYC
Oryctolagus
cuniculus
MD
AID
WI
002
0024
0013
0027
0022
21084
GLQ
NMus
musculus
Chlamydia
trachom
atis
AID
ELISA
21093
GLR
AQD
DFS
GWDI
NTP
AFE
WMus
musculus
Mycobacteriu
mtuberculosis
AID
WI
003
003
0013
003
0032
98453
SGFS
GSV
QFV
Oryctolagus
cuniculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
0037
0027
004
004
1
98453
SGFS
GSV
QFV
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NN
PTCW
AIC
KRIPNKK
Mus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
107107
EAIQ
PRa
ttus
norvegicu
sHom
osapiens
AID
ELISA
107110
EKER
RPSP
IGTA
TLL
Hom
osapiens
MD
OOA
ELISA
005
004
80043
0053
005
110857
FTGEA
YSY
WSA
KHom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
110859
GEE
SRIS
LPLP
NF
SSLN
LRE
Mus
musculus
Hom
osapiens
AID
FIA
00002minus0017
0003
0003
36315
LGSA
YPMus
musculus
Mycobacteriu
mlep
rae
MD
ELISA
36317
LGSG
AFG
TIYK
GMus
musculus
Avian
erythroblasto
sisvirus
AID
ACAb
B001
001
00011
000
9
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122035
WNPA
DYG
GIK
WN
PADYG
GIK
Rattu
snorvegicu
sMD
AID
RIA
001
001
0001
001
000
9
25013
HVA
DID
KLID
Mus
musculus
Puum
alavirus
Kazan
AID
ELISA
25021
HVA
PTH
YVTE
SDA
SQRV
TQL
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
36162
LGIH
EOryctolagus
cuniculus
Cand
idaalbicans
AID
ELISA
36166
LGIM
GE
YRGTP
RNQDLY
DAA
Mus
musculus
Hum
anrespira
tory
virus
AID
RIA
0minus0003minus0007
0minus0001
67253
TWEV
LHMus
musculus
Plasmodium
vivax
AID
ELISA
67257
TWGEN
ETDVLL
LNNTR
PPQ
Hom
osapiens
HepatitisC
virus
OOID
ACAb
Bminus002minus0022minus0027minus0021minus0021
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
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Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
8 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
50990
QGYR
VSSY
LPHom
osapiens
Hevea
brasiliensis
OOA
WI
50998
QHEQ
DR
PTPS
PAP
SRPF
SVL
Hom
osapiens
HepatitisE
virus
OOID
ELISA
001
001minus0002
0007
0008
100458
RDVLQ
LYAPE
Mus
musculus
Bacillusa
nthracis
AID
ELISA
1004
62RF
STRY
GNQNGRI
RVLQ
RFD
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
003
0028
0018
003
003
111036
TEST
FTGEA
YSV
Hom
osapiens
Mycoplasm
apenetra
nsEW
EIR
ELISA
111039
TGVPID
PAVPD
SSIV
PLLE
SBo
staurus
Bovine
papillomaviru
sAID
ELISA
003
0035
002
0033
003
117919
IFIEME
Hom
osapiens
Hom
osapiens
OAID
WI
117921
IGIIDLIE
KRKF
NQ
Mus
musculus
Hom
osapiens
AID
WI
003
0032
003
0037
003
7127
CTDTD
KLF
Oryctolagus
cuniculus
Shigellaflexneri
AID
ELISA
7128
CTDVS
TAIH
ADQL
TPAW
Hom
osapiens
SARS
coronavirus
OOID
ELISA
001
000
6minus0003
000
9001
112253
PGQSP
KLHom
osapiens
Hom
osapiens
OAID
ELISA
112255
PIRA
LVGDEV
ELP
CRISPG
KMus
musculus
Hom
osapiens
AID
ELISA
001
0012
001
0017
001
122034
WNPA
DRa
ttus
norvegicu
sTorpedocalifornica
AID
ELISA
122038
WNPD
DY
GGVKW
NP
DDYG
GVK
Rattu
snorvegicu
sMD
AID
RIA
00minus000
90minus0001
131878
FLMLV
GGST
LHom
osapiens
Hom
osapiens
OAID
ACAb
B131879
FLVA
HT
RARA
PSA
GER
ARR
SMus
musculus
Mus
musculus
AID
NIAA
003
0032
0028
0037
0033
7066
4VQ
VVYD
YQHom
osapiens
Treponem
apallidu
mOOID
ELISA
70667
VQWMNR
LIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
005
004
1005
005
71545
VTV
Hom
osapiens
Helicobacterpylori
OOID
ELISA
71559
VTV
RGGL
RILSPD
RKHom
osapiens
Arachish
ypogaea
OOA
WI
004
004
0032
0043
004
2
127856
TDVRY
KD
Mus
musculus
Mus
musculus
AID
ACAb
B127857
TDVRY
KDDMYH
FFCP
AIQ
AQMus
musculus
Mus
musculus
AID
PFF
001
001
001
001
000
6
112149
GVG
WIRQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
112152
HHPA
RTAHYG
SLP
QKS
HGRT
Hom
osapiens
Hom
osapiens
AID
ELISA
00
00007
0
119581
FSCS
VMHE
Hom
osapiens
Hom
osapiens
OAID
ELISA
119582
GLQ
LIQL
INVDEV
NQI
Mus
musculus
Hom
osapiens
AID
RIAminus001minus0008minus001minus0003minus0011
144657
GQITVD
MMYG
Hom
osapiens
Hom
osapiens
OAID
ELISA
144659
GRE
GYP
ADGGAA
GYC
NTE
Oryctolagus
cuniculus
MD
AID
WIminus001minus000
6minus0017minus0003minus0008
25013
HVA
DI
DKL
IDMus
musculus
Puum
alavirus
Kazan
AID
ELISA
25022
HVA
PTH
YVVES
DA
SQRV
TQV
Hom
osapiens
HepatitisC
virus
OOID
ELISA
0minus0002minus0014minus0001
0
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Research and TreatmentAIDS
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Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 9Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
144652
GMRG
MKG
LVY
Hom
osapiens
Hom
osapiens
OAID
ELISA
144654
GPH
PTLE
VVPM
GRG
SMus
musculus
MD
AID
ELISAminus002minus0018minus0027minus0013minus002
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104521
IGTL
KKIL
DET
VKD
KIAKE
QRa
ttus
norvegicu
sStreptococcus
pyogenes
AID
ELISAminus004minus004minus0053minus004minus004
1
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
7367
CYGDWA
Hom
osapiens
Triticum
aestivum
OOA
ELISA
7374
CYGLP
DS
EPTK
TNGK
Mus
musculus
Tityus
serrulatus
AID
WIminus002minus0018minus0043minus0023minus0018
144610
DFF
TYK
Mus
musculus
Porcine
transmissible
AID
WI
144611
DFN
GSF
DMNGTITA
Oryctolagus
cuniculus
Escherich
iacoli
AID
ELISAminus001minus0007minus0018minus001minus0012
112047
AST
RESG
Hom
osapiens
Hom
osapiens
OAID
ELISA
112048
ATAST
MDHARH
GF
LPRH
RDT
Hom
osapiens
Hom
osapiens
AID
ELISA
005
005
005
0057
005
36136
LGGVFT
Hom
osapiens
Denguev
irus2
OOID
ELISA
36137
LGGWKL
QSD
PRAY
AL
Hom
osapiens
Ambrosia
artemisiifolia
OOA
RIA
001
001
0007
0013
000
9
115256
FREL
KDLK
GY
Hom
osapiens
Bostaurus
DEW
EDWI
115261
GDLE
ILL
QKW
ENG
ECAQ
KKI
Hom
osapiens
Bostaurus
OOA
FIAminus001minus001minus001
0minus000
9
129024
KADQLY
KHom
osapiens
Hom
osapiens
OAID
ELISA
129026
KAKK
PAAAAG
AKK
AKS
Oryctolagus
cuniculus
Hom
osapiens
AID
ELISA
003
0034
003
0037
003
148481
YTRD
LVYK
Rattu
snorvegicu
sHom
osapiens
AID
WI
148483
YVPIVT
FYSE
ISM
HSSRA
IPOryctolagus
cuniculus
MD
AID
ELISA
00002minus0007
0minus0002
150850
GY
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
150853
HIG
GLSI
LDPIFG
VL
Hom
osapiens
Dermatophagoides
farin
aeOOA
ACAb
B004
0038
004
0043
0039
107366
FPPK
PKD
Hom
osapiens
Hom
osapiens
OAID
ELISA
107376
GDRS
GYS
SPGSP
GMus
musculus
Hom
osapiens
AID
ACAb
Bminus003minus0028minus003minus0023minus0031
114859
ICGTD
GVTY
THom
osapiens
Gallusgallus
OOA
WI
114865
IVER
ETR
GQSE
NPL
WHALR
RRa
ttus
norvegicu
sHum
anherpesvirus
AID
ELISA
004
004
20035
0037
0038
62149
SVHLF
Hom
osapiens
MD
OAID
WI
62150
SVIALG
SQEG
ALH
QALA
GAI
Equu
scaballus
WestN
ilevirus
AID
IFAIHminus002minus0021minus0012minus0013minus0021
98455
SGSV
QFV
PIQ
Mus
musculus
Neisseria
meningitid
isAID
ELISA
98456
SICS
NNP
TCWAICK
RIPN
KKMus
musculus
Hum
anrespira
tory
virus
AID
IFAIH
004
004
0027
004
004
1
61783
STNKA
VVS
LSBo
staurus
Bovine
respira
tory
AID
ELISA
61791
STNPK
PQRK
TKRN
TNRR
PQHom
osapiens
HepatitisC
virus
EWEIR
ACAb
B004
0035
0029
0037
0039
100318
NAPK
TFQ
FIN
Mus
musculus
Bacillusa
nthracis
AID
ELISA
100319
NASSEL
HLL
GFG
INAEN
NHR
Hom
osapiens
Arachish
ypogaea
EWED
ELISA
0minus0002minus0012
00
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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Disease Markers
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OncologyJournal of
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Oxidative Medicine and Cellular Longevity
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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
10 Journal of Immunology ResearchTa
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
118947
PFSA
PPPA
Hom
osapiens
Hom
osapiens
OAID
ELISA
118948
PGAIEQG
PADDPG
EGPS
TGP
Hom
osapiens
Hum
anherpesvirus
NI
ACAb
Bminus004minus004minus004
1minus0036minus004
1
78323
YSFR
DMus
musculus
Bluetongue
virus1
AID
ELISA
78341
AALT
AEN
TAIK
KRN
ADAKA
Hom
osapiens
Streptococcus
mutan
sEE
EEL
ISA
001
0008
0007
0013
001
145831
IPLG
TRP
Mus
musculus
Hum
anpapillomaviru
sAID
ELISA
145841
KEDFR
YAISST
NEI
GLL
GA
Susscrofa
Classicalsw
ine
AID
PACminus004minus004minus0045minus004minus0043
119592
HTF
PAVLQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
119596
IHIPSE
KIWRP
DLV
LYMus
musculus
Hom
osapiens
AID
RIA
001
0012
001
0017
000
9
58780
SKAANLSIIK
MD
Beetnecrotic
MD
WI
58783
SKAFSN
CYPY
DVP
DYA
SLOryctolagus
cuniculus
Influ
enza
Avirus
AID
RIAminus001minus000
4minus0014minus000
9minus0013
115153
MKG
VVC
TRIYEK
VHom
osapiens
Hom
osapiens
NI
ELISA
115155
NNQRK
KAKN
TPFN
MLK
RERN
Mus
musculus
Denguev
irus2
AID
ELISA
001
0012
000
40013
001
133629
LPLR
FOryctolagus
cuniculus
Gallusgallus
AID
ACAb
B133630
LPPG
LHV
FPLA
SNRS
Mus
musculus
MD
AID
SPRminus001minus0013minus0021minus001minus001
96215
EEEE
AE
DKE
DHom
osapiens
Hom
osapiens
OAID
ELISA
96216
EEEG
LLK
KSADTL
WNMQK
Mus
musculus
Mus
musculus
AID
ELISA
008
0082
0078
0087
008
39782
LTAASV
Hom
osapiens
Triticum
aestivum
OOA
ELISA
39788
LTAEL
KIYS
VIQ
AEI
NKH
LOryctolagus
cuniculus
Yersiniapestis
AID
ACAb
B001
0014minus0001
0007
000
9
107479
KFNWYV
DHom
osapiens
Hom
osapiens
OAID
ELISA
107482
KGEP
GL
PGRG
FPGFP
Mus
musculus
Hom
osapiens
AID
ACAb
B0
0002
00007minus0001
63569
TETV
NSD
IMacaca
mulatta
Shigellaflexneri
AID
ELISA
63573
TEVEL
KER
KHRIED
AVRN
AK
Hom
osapiens
Mycobacteriu
mlep
rae
OOID
ELISA
005
0036
004
10049
005
134028
DDTIS
Mus
musculus
Hom
osapiens
AID
NIAA
134029
DED
ENQS
PRSFQKK
TROryctolagus
cuniculus
Hom
osapiens
AID
ELISA
005
0053
005
005
004
8
23028
GVKY
AHom
osapiens
MD
OAID
WI
23032
GVLA
KDV
RFSQ
VHom
osapiens
MD
EWEIR
ACAb
B0
00
000
4minus0003
115293
IMCV
KKILDK
Hom
osapiens
Bostaurus
DEW
EDWI
115295
INPS
KEN
LCST
FCK
EVVRN
AHom
osapiens
Bostaurus
OOA
FIAminus003minus003minus003minus002minus0029
65105
TLTP
ENTL
Mus
musculus
Shigellaflexneri
AID
ELISA
65110
TLTS
GSD
LDRC
TTFD
DV
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
001
0013minus0003
001
001
1344
71PK
PEQ
Mus
musculus
Streptococcus
pneumoniae
AID
FACS
1344
72PL
LPGT
STTS
TGPC
KTHom
osapiens
HepatitisB
virus
AID
ELISA
002
0018
0019
002
0016
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
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Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 11Ta
ble3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
142228
LYCY
EQLN
DSSE
Hom
osapiens
Hum
anpapillomaviru
sNI
ELISA
142250
NWGDEP
SKRR
DRS
NSR
GRK
NFelis
catus
Felin
einfectio
usAID
ELISA
007
006
80071
0073
007
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
ERMus
musculus
SARS
coronavirus
AID
ELISA
003
0032
0021
0031
003
21549
GNYD
FWYQ
SHom
osapiens
Staphylococcus
aureus
OOID
ELISA
21553
GNYN
YKY
RYLR
HGK
LRPF
EROryctolagus
cuniculus
SARS
coronavirus
AID
ELISA
003
0034
0021
0031
003
34908
LAPL
GE
Hom
osapiens
HepatitisE
virus
EWEIR
ELISA
34914
LAPS
TLRS
LRKR
RLSSP
Hom
osapiens
Hum
anherpesvirus
OOID
ELISA
006
006
0059
0063
006
130454
CLFP
NNSY
CMus
musculus
MD
AID
NIAA
130456
CRPQ
VN
NPK
EWS
CAAC
Hom
osapiens
MD
OOD
ACAb
B001
0008
001
0011
0007
1964
4GFV
PSM
Hom
osapiens
Hepatitisd
elta
virus
OOID
ELISA
1964
7GFV
SASI
FGFQ
AEV
GPN
NTR
Oryctolagus
cuniculus
VacciniavirusW
RAID
ELISAminus001minus000
6minus0013minus000
9minus001
20678
GKRP
EMus
musculus
Streptococcus
pyogenes
AID
WI
20680
GKS
KRD
AKN
NAAK
LAVDKL
LMus
musculus
VacciniavirusW
RAID
WI
001
001
0001
001
001
123278
GYL
KDLP
TTOvisa
ries
Fasciolahepatica
AID
ELISA
123282
HAC
QKK
LLKF
EAL
QQEE
GEE
Rattu
snorvegicu
sGallusgallus
AID
PFFminus002minus0016minus0016minus002minus0025
141067
PLSLEP
DP
Mus
musculus
Hom
osapiens
AID
FACS
141073
REGVRW
RVMAIQ
Mus
musculus
Hom
osapiens
AID
ELISA
006
006
006
006
0056
139248
SFAG
TVIE
Mus
musculus
Classicalsw
ine
AID
WI
139305
TAAQ
ITQ
RKWEA
ARE
AEQ
RROryctolagus
cuniculus
Hom
osapiens
AID
IP003
0033
0026
003
003
104515
HDCR
PKKI
Mus
musculus
LaCrossevirus
AID
IFAIH
104520
IAKE
QE
NKE
TIGT
LKKI
LDE
Rattu
snorvegicu
sStreptococcus
pyogenes
AID
ELISAminus006minus006minus0073minus006minus0061
113517
HLY
ADGLT
DMus
musculus
Hum
anpapillomaviru
sAID
IFAIH
113518
HNKI
QA
IELE
DLL
RYS
KLYR
Mus
musculus
Hom
osapiens
AID
ELISA
002
002
0011
002
0019
156970
VER
HQ
Hom
osapiens
Hom
osapiens
OAID
ELISA
156975
WSSTV
LRVS
PTRT
VP
Mus
musculus
MD
AID
ELISA
001
0012
0003
0017
001
78252
PVQNLT
Mus
musculus
Porphyromonas
gingivalis
AID
WI
78253
QGGCG
RGWAFS
ATG
AIEA
Mus
musculus
Glycinem
axAID
ELISA
002
002
0017
002
0018
6068
CCPD
KNKS
Mus
musculus
Hum
anherpesvirus
AID
WI
6074
CCRH
KQKD
VGDVK
QTL
PPS
Ovisa
ries
MD
AID
ELISA
001
000
6000
4001
0008
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
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Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
12 Journal of Immunology Research
Table3Con
tinued
New
experim
ent
Experim
ento
freference
Inpu
tperturbationterm
sID
ESequ
ence
hoso
iptq
IDE
Sequ
ence
hoso
iptq
Δ120594ΔΔ120594hoΔΔ120594soΔΔ120594ipΔΔ120594tq
53109
RAGVC
YHom
osapiens
Triticum
aestivum
OOA
ELISA
53116
RAILTA
FSPA
QDI
WGTS
Oryctolagus
cuniculus
SARS
coronavirus
AID
ELISAminus002minus0016minus0037minus0023minus002
147041
IPEQ
Hom
osapiens
Triticum
aestivum
OOA
WI
147064
KHQGA
QYV
WN
RTA
Hom
osapiens
Bostaurus
OOID
WI
006
006
0047
0057
006
7066
4VQ
VVYD
YQOryctolagus
cuniculus
Treponem
apallidu
mAID
ELISA
70667
VQWMN
RLIAFA
FAG
NHVS
PHom
osapiens
HepatitisC
virus
OOID
ELISA
005
004
6004
10049
005
134343
DLY
IKMus
musculus
Hum
anpapillomaviru
sAID
PhDIP
134344
DMAQ
VTV
GPG
LLGVS
TLMus
musculus
Hom
osapiens
AID
PhDIP
00minus000
90
0
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 13
The first input term is the value 120582(120576119894119895)ref is the scoring
function 120582 of the efficiency of the initial process 120576119894119895(known
solution) The function 120582(120576119894119895)ref = 1 if the 119894th peptide could
experimentally be demonstrated to be a B-epitope in theassay of reference (reference) carried out in the conditions119888119895 120582(120576119894119895)ref = 0 otherwise The variational-perturbation
terms ΔΔ120594119888119895are at the same time terms typical of pertur-
bation theory and moving average (MA) functions used inBox-Jenkin models in time series [60] These new types ofterms account both for the deviation of the electronegativityof all amino acids in the sequence of the new peptidewith respect to the peptide of reference and with respectto all boundary conditions In Table 1 we give the overallclassification results obtainedwith thismodel Speck-Plancheet al [61ndash63] introduced different multitargetmultiplexingQSARmodels that incorporate this type of information basedon MAs The results obtained with the present model areexcellent comparedwith other similarmodels in the literatureuseful for other problems including moving average models[64 65] or perturbation models [58] Notably this is also thefirst model combining both perturbation theory and MAs ina QSPR context
The other input terms are the following The first Δ120594seq =120594(119898119902)ref minus120594(119898119894)new is the perturbation term for the variation
or in themean value of electronegativity for all amino acids inthe sequence of the peptide of reference The remnant inputvariables of the model ΔΔ120594
119888119895= Δ120594
119888119895-ref minus Δ120594119888119895-new =
[120594(119898119902)ref minus
lowast120594 (119888119902119903)ref] minus [120594(119898119894)new minus
lowast120594 (119888119894119895)new] quantify
values of the conditions of the new assay 119888119895-new that representperturbations with respect to the initial conditions 119888
119894119895-ref of
the assay of reference The quantities lowast120594(119888119894119895) and lowast120594(119888
119902119903) are
the average values of the mean electronegativity values 120594(119898119894)
and 120594(119898119902) for all new and reference peptides in IEDB that
are epitopes under the 119895th or 119903th boundary condition Thevalues of these terms have been tabulated for gt500 sourceorganisms gt50 host organisms gt10 biological process andgt30 experimental techniques We must substitute the valuesof 120594(119898
119894) and 120594(119898
119902) of the new and reference peptides and the
tabulated values of lowast120594(119888119894119895) and lowast120594(119888
119902119903) for all combinations
of boundary conditions to predict the perturbations of theaction as epitope of peptides In doing so we can found theoptimal sequence and boundary conditions towards the useof the peptide in the development of a vaccine In Table 2 wegive some of these values of lowast120594(119888
119894119895) and lowast120594(119888
119902119903)
In Table 3 we depict the sequences and input-outputboundary conditions for top perturbations present in IEDBAll these perturbations have observed value of 120582(120576
119894119895)new = 1
and predicted value also equal to 1 with a high probabilitySee Supplementary Material available online at httpdxdoiorg1011552014768515 file contains a full list of gt200000cases of perturbations
4 Conclusions
It is possible to develop generalmodels for vaccine design ableto predict the results of multiple input-output perturbations
in peptide sequence and experimental assay boundary condi-tions using ideas of QSPR analysis perturbation theory andBox and Jenkins MA operators The electronegativity valuescalculated withMARCH-INSIDE seem to be goodmoleculardescriptors for this type of QSPR-perturbation models
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The present study was partially supported by GrantsAGL2010-22290-C02 and AGL2011-30563-C03 from Minis-terio de Ciencia e Innovacion Spain and Grant CN 2012155from Xunta de Galicia Spain
References
[1] B Peters J Sidney P Bourne et al ldquoThe design and implemen-tation of the immune epitope database and analysis resourcerdquoImmunogenetics vol 57 no 5 pp 326ndash336 2005
[2] B Peters J Sidney P Bourne et al ldquoThe immune epitopedatabase and analysis resource from vision to blueprintrdquo PLoSBiology vol 3 no 3 p e91 2005
[3] PWang A AMorgan Q Zhang A Sette and B Peters ldquoAuto-mating document classification for the Immune Epitope Data-baserdquo BMC Bioinformatics vol 8 article 269 2007
[4] A Sette ldquoThe immune epitope database and analysis resourcefrom vision to blueprintrdquo Genome Informatics vol 15 no 2 p299 2004
[5] N Salimi W Fleri B Peters and A Sette ldquoThe immune epi-tope database a historical retrospective of the first decaderdquoImmunology vol 137 no 2 pp 117ndash123 2012
[6] Y Kim A Sette and B Peters ldquoApplications for T-cell epitopequeries and tools in the Immune EpitopeDatabase andAnalysisResourcerdquo Journal of Immunological Methods vol 374 no 1-2pp 62ndash69 2011
[7] Y Kim J Ponomarenko Z Zhu D Tamang et al ldquoImmuneepitope database analysis resourcerdquo Nucleic Acids Research ppW525ndashW530 2012
[8] A M Helguera R D Combes M P Gonzalez and M N DS Cordeiro ldquoApplications of 2D descriptors in drug design aDRAGON talerdquo Current Topics in Medicinal Chemistry vol 8no 18 pp 1628ndash1655 2008
[9] GM Casanola-Martın Y Marrero-Ponce M T H Khan et alldquoDragon method for finding novel tyrosinase inhibitors Biosil-ico identification and experimental in vitro assaysrdquo The Euro-pean Journal of Medicinal Chemistry vol 42 no 11-12 pp 1370ndash1381 2007
[10] I V Tetko J Gasteiger R Todeschini et al ldquoVirtual computa-tional chemistry laboratorymdashdesign and descriptionrdquo Journalof Computer-AidedMolecular Design vol 19 no 6 pp 453ndash4632005
[11] A R Katritzky A Oliferenko A Lomaka and M KarelsonldquoSix-membered cyclic ureas as HIV-1 protease inhibitors aQSAR study based on CODESSA PRO approachrdquo Bioorganicand Medicinal Chemistry Letters vol 12 no 23 pp 3453ndash34572002
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
14 Journal of Immunology Research
[12] A R Katritzky S Perumal R Petrukhin and E Klein-peter ldquoCODESSA-based theoretical QSPRmodel for hydantoinHPLC-RT lipophilicitiesrdquo Journal of Chemical Information andComputer Sciences vol 41 no 3 pp 569ndash574 2001
[13] S Vilar G Cozza and S Moro ldquoMedicinal chemistry and themolecular operating environment (MOE) application of QSARand molecular docking to drug discoveryrdquo Current Topics inMedicinal Chemistry vol 8 no 18 pp 1555ndash1572 2008
[14] G Marzaro A Chilin A Guiotto et al ldquoUsing the TOPS-MODE approach to fit multi-target QSAR models for tyrosinekinases inhibitorsrdquo The European Journal of Medicinal Chem-istry vol 46 no 6 pp 2185ndash2192 2011
[15] S Vilar E Estrada E Uriarte L Santana and Y Gutierrez ldquoInsilico studies toward the discovery of new anti-HIV nucleosidecompounds through the use of tops-mode and 2D3D con-nectivity indices 2 Purine derivativesrdquo Journal of ChemicalInformation and Modeling vol 45 no 2 pp 502ndash514 2005
[16] E Estrada J A Quincoces and G Patlewicz ldquoCreating molec-ular diversity from antioxidants in Brazilian propolis combina-tion of TOPS-MODE QSAR and virtual structure generationrdquoMolecular Diversity vol 8 no 1 pp 21ndash33 2004
[17] E Estrada andHGonzalez ldquoWhat are the limits of applicabilityfor graph theoretic descriptors in QSPRQSAR modelingdipole moments of aromatic compounds with TOPS-MODEdescriptorsrdquo Journal of Chemical Information and ComputerSciences vol 43 no 1 pp 75ndash84 2003
[18] Y Marrero-Ponce J A Castillo-Garit E Olazabal et al ldquoTom-ocomd-Cardd a novel approach for computer-aided ldquorationalrdquodrug design I Theoretical and experimental assessment of apromising method for computational screening and in silicodesign of new anthelmintic compoundsrdquo Journal of Computer-Aided Molecular Design vol 18 no 10 pp 615ndash634 2004
[19] Y Marrero Ponce R Medina Marrero E A Castro et al ldquoPro-tein quadratic indices of the ldquomacromolecular pseudographrsquos 120572-carbon atom adjacency matrixrdquo 1 prediction of arc repressoralanine-mutantrsquos stabilityrdquoMolecules vol 9 no 12 pp 1124ndash11472004
[20] H Gonzalez-Dıaz F Prado-Prado and F M Ubeira ldquoPredict-ing antimicrobial drugs and targets with the MARCH-INSIDEapproachrdquo Current Topics in Medicinal Chemistry vol 8 no 18pp 1676ndash1690 2008
[21] R Garcıa-Domenech J Galvez J V de Julian-Ortiz and LPogliani ldquoSomenew trends in chemical graph theoryrdquoChemicalReviews vol 108 no 3 pp 1127ndash1169 2008
[22] E Estrada and E Uriarte ldquoRecent advances on the role of topo-logical indices in drug discovery researchrdquo Current MedicinalChemistry vol 8 no 13 pp 1573ndash1588 2001
[23] R Todeschini andVConsonniHandbook ofMolecularDescrip-tors Wiley-VCH Weinheim Germany 2008
[24] E Estrada E J Delgado J B Alderete and G A Jana ldquoQuan-tum-connectivity descriptors inmodeling solubility of environ-mentally important organic compoundsrdquo Journal of Computa-tional Chemistry vol 25 no 14 pp 1787ndash1796 2004
[25] E Besalu X Girones L Amat andR Carbo-Dorca ldquoMolecularquantum similarity and the fundamentals of QSARrdquo Accountsof Chemical Research vol 35 no 5 pp 289ndash295 2002
[26] D A Rincon M N D S Cordeiro and R A Mosquera ldquoOnthe electronic structure of cocaine and its metabolitesrdquo Journalof Physical Chemistry A vol 113 no 50 pp 13937ndash13942 2009
[27] M Mandado M J Gonzalez-Moa and R A MosqueraldquoChemical graph theory and n-center electron delocalization
indices a study on polycyclic aromatic hydrocarbonsrdquo Journalof Computational Chemistry vol 28 no 10 pp 1625ndash1633 2007
[28] THill andP Lewicki STATISTICSMethods andApplications AComprehensive Reference for Science Industry and DataMiningStatSoft Tulsa Okla USA 2006
[29] E Frank M Hall L Trigg G Holmes and I H Witten ldquoDatamining in bioinformatics using Wekardquo Bioinformatics vol 20no 15 pp 2479ndash2481 2004
[30] G Patlewicz N Ball E D Booth E Hulzebos E Zvinavasheand C Hennes ldquoUse of category approaches read-across and(Q)SAR general considerationsrdquo Regulatory Toxicology andPharmacology vol 67 no 1 pp 1ndash12 2013
[31] D W Roberts and G Y Patlewicz ldquoUpdating the skin sensiti-zation in vitro data assessment paradigm in 2009mdasha chemistryand QSAR perspectiverdquo Journal of Applied Toxicology vol 30no 3 pp 286ndash288 2010
[32] D W Roberts and G Y Patlewicz ldquoNonanimal alternatives forskin sensitization letter to the editorrdquoToxicological Sciences vol106 no 2 pp 572ndash574 2008
[33] E Estrada G Patlewicz and Y Gutierrez ldquoFrom knowledgegeneration to knowledge archive a general strategy usingTOPS-MODE with DEREK to formulate new alerts for skinsensitizationrdquo Journal of Chemical Information and ComputerSciences vol 44 no 2 pp 688ndash698 2004
[34] G F Gerberick C A Ryan P S Kern et al ldquoA chemical datasetfor evaluation of alternative approaches to skin-sensitizationtestingrdquo Contact Dermatitis vol 50 no 5 pp 274ndash288 2004
[35] G Y Patlewicz D A Basketter C K Smith Pease et al ldquoFurtherevaluation of quantitative structurendashactivity relationship mod-els for the prediction of the skin sensitization potency ofselected fragrance allergensrdquo Contact Dermatitis vol 50 no 2pp 91ndash97 2004
[36] E Estrada G Patlewicz M Chamberlain D Basketter andS Larbey ldquoComputer-aided knowledge generation for under-standing skin sensitization mechanisms the TOPS-MODEapproachrdquo Chemical Research in Toxicology vol 16 no 10 pp1226ndash1235 2003
[37] G Patlewicz R Rodford and J D Walker ldquoQuantitative struc-ture-activity relationships for predicting skin and eye irritationrdquoEnvironmental Toxicology and Chemistry vol 22 no 8 pp1862ndash1869 2003
[38] G Y Patlewicz R A Rodford G Ellis and M D Barratt ldquoAQSAR model for the eye irritation of cationic surfactantsrdquo Tox-icology In Vitro vol 14 no 1 pp 79ndash84 2000
[39] E Tenorio-Borroto C G Penuelas Rivas J C VasquezChagoyan N Castanedo F J Prado-Prado and X Garcia-Mera ldquoANN multiplexing model of drugs effect on macro-phages theoretical and flow cytometry study on the cyto-toxicity of the anti-microbial drug G1 in spleenrdquo Bioorganic ampMedicinal Chemistry vol 20 no 20 pp 6181ndash6194 2012
[40] D R Flower H McSparron M J Blythe et al ldquoComputationalvaccinology quantitative approachesrdquo Novartis FoundationSymposium vol 254 pp 102ndash120 2003
[41] I A Doytchinova P Guan and D R Flower ldquoQuantitativestructure-activity relationships and the prediction of MHCsupermotifsrdquoMethods vol 34 no 4 pp 444ndash453 2004
[42] Y Xiao and M R Segal ldquoPrediction of genomewide conservedepitope profiles of HIV-1 classifier choice and peptide rep-resentationrdquo Statistical Applications in Genetics and MolecularBiology vol 4 no 1 article 25 2005
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Journal of Immunology Research 15
[43] T Fagerberg V Zoete S Viatte P Baumgaertner P M Alves PRomero et al ldquoPrediction of cross-recognition of peptide-HLAA2 by melan-a-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationshipsrdquo PLoSONE vol 8 no 7 Article ID e65590 2013
[44] D Barh A N Misra A Kumar and A Vasco ldquoA novel strategyof epitope design inNeisseria gonorrhoeaerdquo Bioinformation vol5 no 2 pp 77ndash82 2010
[45] J Bi R Song H Yang et al ldquoStepwise identification of HLA-Alowast0201-restricted CD8+ T-cell epitope peptides from herpessimplex virus type 1 genome boosted by a steprank schemerdquoBiopolymers vol 96 no 3 pp 328ndash339 2011
[46] R D Bremel and E J Homan ldquoAn integrated approach to epit-ope analysis II a system for proteomic-scale prediction ofimmunological characteristicsrdquo Immunome Research vol 6 no1 article 8 2010
[47] C M Diez-Rivero B Chenlo P Zuluaga and P A RecheldquoQuantitative modeling of peptide binding to TAP using sup-port vector machinerdquo Proteins vol 78 no 1 pp 63ndash72 2010
[48] E Martınez-Naves E M Lafuente and P A Reche ldquoRecogni-tion of the ligand-type specificity of classical and non-classicalMHC I proteinsrdquo FEBS Letters vol 585 no 21 pp 3478ndash34842011
[49] M Bhasin E L Reinherz and P A Reche ldquoRecognition andclassification of histones using support vectormachinerdquo Journalof Computational Biology vol 13 no 1 pp 102ndash112 2006
[50] P A Reche and E L Reinherz ldquoPEPVAC a web server formulti-epitope vaccine development based on the prediction ofsupertypic MHC ligandsrdquo Nucleic Acids Research vol 33 no 2pp W138ndashW142 2005
[51] E M Lafuente and P A Reche ldquoPrediction of MHC-peptidebinding a systematic and comprehensive overviewrdquo CurrentPharmaceutical Design vol 15 no 28 pp 3209ndash3220 2009
[52] A Gaulton L J Bellis A P Bento et al et al ldquoA large-scalebioactivity database for drug discoveryrdquoNucleic Acids Researchpp D1100ndashD1107 2012
[53] W H Cropper Great Physicists The Life and Times of LeadingPhysicists from Galileo to Hawking Oxford University PressNew York NY USA 2004
[54] E L Bouzarth A Brooks R Camassa et al ldquoEpicyclic orbits ina viscous fluid about a precessing rod theory and experimentsat the micro-and macro-scalesrdquo Physical Review E vol 76 no1 part 2 Article ID 016313 2007
[55] M Laberge ldquoIntrinsic protein electric fields basic non-covalentinteractions and relationship to protein-induced Stark effectsrdquoBiochimica et BiophysicaActa vol 1386 no 2 pp 305ndash330 1998
[56] FM Fernandez and J AMorales ldquoPerturbation theorywithoutwave functions for the Zeeman effect in hydrogenrdquo PhysicalReview A vol 46 no 1 pp 318ndash326 1992
[57] B D Marshall and W G Chapman ldquoThermodynamic pertur-bation theory for associating fluids with small bond angleseffects of steric hindrance ring formation and double bondingrdquoPhysical Reiew E vol 87 no 5 Article ID 052307 12 pages 2013
[58] H Gonzalez-Diaz S Arrasate A Gomez-San Juan et al ldquoNewtheory for multiple input-output perturbations in complexmolecular systems 1 Linear QSPR electronegativity models inphysical organic and medicinal chemistryrdquo Current Topics inMedicinal Chemistry 2013
[59] H Gonzalez-Diaz S Arrasate N Sotomayor et al ldquoMIANNmodels in medicinal physical and organic chemistryrdquo CurrentTopics in Medicinal Chemistry vol 13 no 5 pp 619ndash641 2013
[60] G E P Box and GM Jenkins Time Series Analysis Forecastingand Control Holden-Day San Francisco Calif USA 1970
[61] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoUnified multi-target approach for the rational insilico design of anti-bladder cancer agentsrdquo Anti-Cancer Agentsin Medicial Chemistry vol 13 no 5 pp 791ndash800 2013
[62] A Speck-Planche V V Kleandrova and M N Cordeiro ldquoNewinsights toward the discovery of antibacterial agents multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugsrdquo TheEuropean Journal of Pharmaceutical Sciences vol 48 no 4-5pp 812ndash818 2013
[63] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoMulti-target inhibitors for proteins associated withAlzheimer in silico discovery using fragment-based descrip-torsrdquo Current Alzheimer Research vol 10 no 2 pp 117ndash1242013
[64] A Speck-Planche V V Kleandrova F Luan and M NCordeiro ldquoIn silico discovery and virtual screening of multi-target inhibitors for proteins in Mycobacterium tuberculosisrdquoCombinatorial Chemistry amp High Throughput Screening vol 15no 8 pp 666ndash673 2012
[65] A Speck-Planche V V Kleandrova F Luan and M N DS Cordeiro ldquoChemoinformatics in anti-cancer chemotherapymulti-target QSAR model for the in silico discovery of anti-breast cancer agentsrdquo The European Journal of PharmaceuticalSciences vol 47 no 1 pp 273ndash279 2012
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
Submit your manuscripts athttpwwwhindawicom
Stem CellsInternational
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MEDIATORSINFLAMMATION
of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Behavioural Neurology
EndocrinologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Disease Markers
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BioMed Research International
OncologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Oxidative Medicine and Cellular Longevity
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
PPAR Research
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Journal of
ObesityJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Computational and Mathematical Methods in Medicine
OphthalmologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Diabetes ResearchJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Research and TreatmentAIDS
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Gastroenterology Research and Practice
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Parkinsonrsquos Disease
Evidence-Based Complementary and Alternative Medicine
Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom
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