In silico identification of B- and T-cell epitopes on OMPLA and...

Indian Journal of Biotechnology Vol 10, October 2011, pp 440-451

In silico identification of B- and T-cell epitopes on OMPLA and LsrC from Salmonella typhi for peptide-based subunit vaccine design

K Prabhavathy, P Perumal and N SundaraBaalaji* Department of Bioinformatics, School of Life Sciences, Bharathiar University, Coimbatore 641 046, India

Typhoid, caused by Salmonella typhi, has been the most common human illness. In the present study, peptide-based subunit vaccine was developed from OMPLA and LsrC against S. typhi. We adopted sequence, 3-D structure and fold level in silico analysis to predict B-cell and T-cell epitopes. The 3-D structure was determined for OMPLA and LsrC by homology modeling and the modeled structure was validated. One T-cell epitope from OMPLA (WQLSNSKES, 99.5%) and one from LsrC (FIPNQTGTG, 75.31%) binds to maximum number of MHC class I and class II alleles. They also specifically bind to HLA alleles, namely, A*0201, A*0204, B*2705, DRB1*0101 and DRB1*0401. Molecular dynamics simulation of DRB1*0401-epitope complex indicated that they are stable and flexible. Taken together, the results indicate that OMPLA and LsrC are more suitable vaccine candidates against typhoid. Similar epitopes from other human pathogens were identified, which may provide useful information for vaccine development.

Keywords: Computational prediction, DRB1*0401, epitope, epitope design, immunoinformatics, OMPLA, LsrC, MHC, typhoid

Introduction Typhoid is a common illness causing high fever, headache, weakness, stomach pains, loss of appetite, cough and sometimes a rash. It is caused by Salmonella typhi, a Gram-negative bacterium belongs to the family Enterobacteriaceae1. Typhoid has been identified as a serious health problem by WHO and according to its estimate about 216,000 deaths occur annually in endemic areas throughout Africa and Asia, and pathogen persists in the Middle East and a few southern and eastern European countries2. Quorum Sensing (QS) is a widespread mechanism of cell-cell communication used by S. typhi to monitor the cell density and to induce or repress gene in response to changes in the cell number3. During QS, bacteria produce, secrete and detect signaling molecules called autoinducers (AI)4-6. AI-2 was recently shown to control a seven-gene operon, called the LuxS regulated operon (Lsr)7. Four of the Lsr operon genes (ABCD) encode ABC transporter whose function is to promote the internalization of AI-2. There are also other membrane proteins that are hydrolytic in nature like outer membrane phospholipase A (OMPLA) that acts as a sensor to

monitor changes in physical properties of the membrane. These surface-exposed membrane proteins maintain the integrity of outer membrane (OMPLA), control expression of genes like type IV pili-toxin correlated-pilus (LsrC), thereby determining the pathogenicity of the bacteria. Vaccines offer protection against infectious disease8. Capsular polysaccharide vaccines are available for the prevention of infection caused by Neisseria meningitides. But still infection is highly prevalent in industrialized countries due to poor immunogenicity of the capsular polysaccharide9. Advantage of a protein or peptide-based vaccine is the ability to deliver high doses of the potential immunogen and at a low cost10. Protein which could act as a vaccine candidate must be surface-exposed, antigenic and responsible for pathogenicity11. Single protein can be used as a drug target as well as vaccine candidate against diseases. However, whole protein is not essential for raising the immune response and small segments of protein or epitopes are adequate to elicit an immune response12. Peptide-based subunit vaccine has recently attracted attention in both treating infectious diseases and also for promoting destruction of cancerous cells13. These type of vaccines are easy to produce and also safe when compared to the usual vaccines like killed vaccine and attenuated vaccine.

_________ *Author for correspondence: Tel: 91-422-2428285; Fax: 91-422-2422387/2425706 E-mail: [email protected]; [email protected]

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Immune response of peptides is also checked to confirm whether they can be used as a vaccine candidate. Immune response depends on binding of epitopes to HLA alleles, major histocompatibility complex (MHC) class I (recognize CD8+ T-cells) and MHC class II (recognize CD4+ T-cells) molecules14,15. Activation of both helper T-lymphocytes (HTL) and cytotoxic T-lymphocytes (CTL) requires recognition of specific peptides bound to MHC molecules on the antigen presenting cells (APCs)/target cell. Protein antigens inside the APCs/target cell are degraded into small peptide fragments by the intracellular proteases. After partial proteolysis, some of the peptides, called T-cell epitopes, bind to MHC molecules and are transported to the cell surface for recognition by the antigen specific T-cell receptors. Thus, MHC binding is a pre-requisite for a peptide to be a T-cell epitope16. Peptide should lack proteosomal recognition site if used as a vaccine candidate, otherwise it degrades during antigen processing17. Whole cell vaccines like Ty2 are available but they are associated with side effects and generally being abandoned18. Therefore, for the development of an effective vaccine against typhoid, we have focused on the membrane proteins of S. typhi, which can elicit both B-cell and T-cell mediated immunity. In the present work, we have selected two proteins, OMPLA and LsrC, from S. typhi for designing peptide-based subunit vaccine. The aim of the study was to predict B-cell and T-cell epitopes and stability of the MHC class II-epitope complex. The predicted epitopes may then be used as a peptide-based subunit vaccine candidate against multiple pathogens.

Materials and Methods

Target Protein Sequence Retrieval

The amino acid sequences of OMPLA (P0A232) and LsrC (Q8Z2X6) were retrieved from Swiss-Prot protein database (http://us.expasy.org/sport). OMPLA and LsrC were selected as vaccine candidate for designing the epitope, where the epitope was able to elicit both the humoral mediated response (B-cell) and cell mediated immunity (T-cell). Identification of B-cell and T-cell Epitope

Protein sequences of OMPLA and LsrC were subjected for B-cell epitope prediction using BCPreds20. Both BCPred and AAP prediction methods21 of BCPreds were used to identify common B-cell epitope. B-cell epitope having BCPreds score

>0.8 and VaxiJen score >0.4 were selected for prediction of T-cell epitopes. Selected B-cell epitope were then subjected to ProPred122 and ProPred16

analyses. ProPred allows to predict MHC class II binding peptides (HTL epitopes) for 51 alleles and ProPred1 to predict MHC class I binding peptides (CTL epitopes) for 47 alleles. Common epitope that bind to both the MHC classes were selected. The selected epitope was then analyzed with VaxiJen v2.0 server. The IC50 value of corresponding epitopes was deduced from MHCPred server23. Epitopes with IC50 value less than 1000 nM for allele DRB1*0101 of MHC class II were selected. Structure based QSAR simulation methods using T-epitope Designer24 and MHCPred are the second screening methods. T-epitope Designer predicts HLA-peptide binding based on virtual binding pockets using X-ray crystal structures of HLA-peptide complexes, followed by calculation of peptide binding to binding pockets. In the second screening, the selection criteria were: i) binding with large number of HLA alleles (>1000), ii) must bind to DRB1*0101 and DRB1*0401, and iii) must bind to A*0201, A*0204 and A*2705. The final list of epitopes was selected based on the above mentioned criteria and VaxiJen score. Selected epitopes were further analyzed for ‘fold level’ topology.

Homology Modeling and Fold Level Topology Analysis

Homology modeling of OMPLA and LsrC was carried out using Modeller 9v725 and I-TASSER26, respectively. The best model was selected based on RMSD score, energy minimization value, Prosa-web27 and Ramachandran plot and these were carried out using PROCHECK28 tool at Swiss-Model server (http://swissmodel.expasy.org./). The folding and clusters of selected epitopes in folded protein were analyzed to confirm the topology of epitope using Pepitope server29. The server uses PepSurf and Mapitope algorithms to analyse the fold level topology in the protein. PepSurf algorithm30 maps the epitopes onto the surface of the antigen. Mapitope algorithm31 is based on a computational approach in which epitope shared by the entire set of peptides are detected. The minimal input requirement for both algorithms is epitope sequences and a modeled structure of the antigen. Using these two inputs the following steps were carried out: (1) The epitope prediction algorithm was executed. (2) The predicted epitopes were visualized on the 3-D structure through 3-D structural viewer.

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Characterization of Epitopes

Due to the short sequence of epitopes from S. typhi, OMPLA and LsrC were modeled using DISTILL server32 that can predict 3-D structure of small fragments of proteins based on the similarity with Protein Data Bank (PDB) template. Resultant epitopes were then validated with ProSA-web and PROCHECK. ProFunc33 and Motif Scan (http://myhits.isb-sib.ch/cgibin/motif_scan) and InterProScan (http://www.ebi.ac.uk/Tools/pfa/iprscan/) were used to predict domain, motif and functionality of epitopes. ProteinDigest (http://db.systemsbiology. net:8080/proteomics Toolkit/proteinDigest.html) was used to determine mol wt, pI and enzymatic degradation site of epitopes.

Identification of Common Epitope from Multiple Pathogens

The selected S. typhi T-cell epitope was aligned against OMPLA and LsrC sequences from other pathogens using multiple sequence alignment tool, Clustal W. The final selection of epitopes from other human pathogen were based on VaxiJen score (antigenicity), MHCPred (DRB1*0101 and DRB1*0401) and T-epitope designer (A*0201, A*0204 and A*2705) analysis. A summary of in silico approach is shown in Fig. 1.

Docking and MD Simulation

The epitope from OMPLA and LsrC were docked with the DRB1*0401 (PDB ID: 1D5M) using GLIDE34. GLIDE searches for favourable interactions between epitope and MHC molecule. GLIDE was run in flexible docking modes. GLIDE generates conformations internally and passes these through a series of filters. The OPLS force field was used for evaluation and refinement of docking solutions. MD simulations were carried out using MacroModel35, as distributed by Schrodinger (http://www.schrodinger.com/). We performed 1ns MD simulations for DRB1*0401-epitope complex (DRB1*0401-WQLSNSKES, DRB1*0401-FIPNQTGTG) in explicit water, using the OPLS_2005 force field. All simulations were performed at constant temperature (300 K) and an integration step of 1.5 fs was used. Coordinate’s energy was saved for every 100 ps upto 1ns. All graphs were visualized using ScatterPlot.

Results and Discussion OMPLA is an integral outer membrane enzyme, while LsrC is present on S. typhi inner membrane. OMPLA is involved in the secretion of bacteriocins. In Campylobacter coli, it is a major hemolytic factor36, while it is involved in the invasion of the

gastric mucosa and causes tissue damage in

Helicobacter pylori37. Therefore, they are important

targets for developing vaccine. In the present study, various bioinformatics tools have been used to identify the potential epitopes from S. typhi OMPLA and LsrC, which can induce both B-cell and T-cell mediated immunity.

Antigenicity of Selected Protein

VaxiJen is the antigenicity prediction server, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. The leave-one-out cross validation (LOO-CV) was used to identify antigenicity of proteins with 82% accuracy, 91% sensitivity and 72% specificity for bacterial species. OMPLA and LsrC protein were analyzed for

Fig. 1––In silico approach for B-cell and T-cell epitope identification


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antigenicity using VaxiJen server. The score obtained for OMPLA and LsrC was 0.5326 and 0.4732 respectively, showing that proteins were antigenic. Identification of Peptide Vaccine

Epitopes, which induce both B-cell and T-cell mediated immunity, are known to be good vaccine candidates16. To identify such epitopes, amino acid sequence of OMPLA and LsrC were subjected to BCPred for B-cell epitope prediction. B-cell epitope identification is the first step in epitope designing38. BCpreds that uses a novel method of subsequence kernel was used to predict linear B-cell epitopes from each protein. BCPred server has three prediction methods. However, we selected BCPred and AAP (amino acid pair) method for predicting fixed length epitopes21. Epitopes having BCpreds and VaxiJen scores >0.8 and >0.4, respectively were selected. Finally 6 out of 13 B-cell epitopes from OMPLA and 4 out of 8 epitopes from LsrC were selected for further analysis (Table 1). Selected B-cell epitopes were analyzed for identification of T-cell epitopes. For the first level,

sequence-based 2D screening, ProPredI, ProPred and MHCPred were used to identify the T-cell epitopes. T-cell epitopes were identified using ProPred1 and ProPred with default parameters. The ProPred and ProPred1 implement matrix-based prediction algorithm. The obtained matrices are multiplication matrices, where the scores are calculated by multiplying and summing the score of each amino acid position. MHCPred uses partial least squares (PLS) based approach for the prediction of binding affinity to MHC molecules, which was used in the present study. Earlier, malarial merozoite surface protein-1 T-cell epitopes were identified by using MHCPred39. MHCPred with the combination of SYFPEITHI, NetMHC servers have also been used for Epstein-Barr virus latent membrane protein-2A T-cell epitope prediction40. The predicted output is given in units of IC50 nM. A lower value of peptide IC50 indicates higher affinity towards MHC molecules. Common epitopes that bind to maximum number of both the MHC class I and II, and specifically interact with DRB1*0101 are listed in Table 2. One epitope out of seven from OMPLA and

Table 1––Selected B-cell epitopes using both the modules of BCPreds (BCPred+AAP) and antigenicity of protein using VaxiJen

Proteins Amino acid position BCPred epitope sequence BCPred scores Vaxijen score

OMPLA 138 170 142 209 230 98

MGYNHDSNGRSDPTSRSWNR LVEVKPWYVIGSTDDNPDIT HDSNGRSDPTSRSWNRLYTR SAKGQYNWNTGYGGAEVGLS PVTKHVRLYTQVYSGYGESL WQLSNSKESSPFRETNYEPQ

0.957 0.915

1 0.983

1 1

1.2366 1.0999 1.0272 1.0060 0.6319 0.7173

LsrC 181 325 179 225

AFGRNFYATGDNLQGARQLG SPPTPLQAEAKTHAQQNKNK KTAFGRNFYATGDNLQGARQ FASQIGFIPNQTGTGLEMKA

0.957 1 1

0.983

0.7259 0.9499 0.8208 0.8208

Table 2––Common epitope that can induce both B-cell and T-cell mediated immunity are represented alongwith their various parameters (Selected epitopes are highlighted in bold)

Proteins Epitopes Amino acid

position VaxiJen score IC50 value of epitopes for

DRB1*0101 (MHCPred) Total no. of MHC binding

allele (ProPred I & ProPred)

OMPLA

YNHDSNGRS LVEVKPWYV YNWNTGYGG

140 170 214

2.8144 2.2631 0.9352

16.18 29.58

105.68

8 6 3

YGGAEVGLS VRLYTQVYS VTKHVRLYT WQLSNSKES

220 235 231 98

2.0744 0.4768 0.8059 1.0261

24.60 166.72 431.52 47.64

11 38 4

28

LsrC FYATGDNLQ FIPNQTGTG

186 231

1.2674 0.9352

16.18 105.68

2 7


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one epitope out of two from LsrC were selected based on VaxiJen score and MHCPred score, and also based on surface localisation of epitope in theoretical model. At the second level of structure and QSAR-based screening, identified epitopes in the first screen were used to predict their binding abilities to >1000 MHC alleles using T-epitope Designer. The T-epitope Designer is based on a model that defines peptide binding pockets, using X-ray information from crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. The server predicts HLA-peptide binding complexes. The epitopes that bind to >75% alleles were selected (Table 3). These epitopes also bind to DRB1*0401

alleles of MHC class-II. DRB1*0401 allele gives resistance against typhoid fever41. A complex between this allele and selected epitope was studied for their stability using MD simulations. Homology Modeling and Structure Refinement

The 3-D structures of OMPLA and LsrC were not available in PDB. So the 3-D structure of OMPLA was constructed using homology modeling server Modeller9v7 (Fig. 2). The homology modeling of OMPLA was performed with X-ray structure of OMPLA from Escherichia coli (PDB ID-1QD5) as a template. The LsrC protein sequence has a length of 347 aa and it showed less than 30% similarity with the template sequence. So, the sequence was subjected to Ab initio modeling (I-TASSER). The modeled LsrC protein’s C score was found to be –3.53 Å (Fig. 3). To validate the model, ProSA-web was used that compares and analyses the energy distribution in protein structure as a function of sequence position to determine a structure as native-like or fault. As shown in Fig. 4 and the Z-scores, the OMPLA model found to be a structure of good quality. However, in case of LsrC, ProSA-web cannot be performed because the protein was modeled by Ab initio modeling. According to Ramchandran Plot,

Table 3––3-D QSAR based T-cell epitope prediction using T-epitope Designer

Proteins Epitopes % of

binders Lowest score

Highest score

OMPLA WQLSNSKES 99.5 87.19

(A*2434) 3926.97

(B*0805)

LsrC FIPNQTGTG 75.3 5.37 (B*4410)

2180.23 (B*0814)

Fig. 2––Three-dimensional structure of OMPLA from S. typhi modelled using Modeller9v7 [The OMPLA (1QD5) from E. coli was used as template]

Fig. 3––Ab initio model of the LsrC from S. typhi modeled using I-TASSER server


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the modeled OMPLA protein has 89.3% of the amino acid residues in the most favoured regions, 10.3 % in additional allowed regions and 0.4 % in generously allowed regions (Fig. 5). The RMSD value was found to be 0.137 Å. For LsrC protein, Ramchandran Plot shows 80.9% of the amino acid residues in the most favoured regions, 14.4% at additional allowed regions, 2.3% in generously allowed regions and 2.3% in disallowed regions (Fig. 6). These values ensure the geometrically acceptable quality of the OMPLA and LsrC models (Figs 2 & 3). Topology and Characterisation of T-cell Epitopes

Position of predicted epitopes on the theoretical models of OMPLA and LsrC were identified using

Peptitope server. The Peptiope server predicts epitopes based on a set of peptides those are affinity selected against a monoclonal antibody or peptides extracted from a phage display library. It also aligns a linear peptide sequence onto a 3-D protein structure. The present study shows that the epitopes were present within the clusters and all the epitopes were located on the outside of cell (Figs 7a & b). The OMPLA protein is antigenic and one epitope “WQLSNSKES” from Cluster-I (Score: 9.6719, Residue No: 8) was found to be antigenic (VaxiJen score: 1.0261) and can bind 28 MHC molecules of both the MHC class I and II. The IC50 value of this epitope for DRB1*0101 and DRB1*0401 was 47.64 and 36.64 nM, respectively, which indicates a good

Fig. 4(a-d)––Validation of 3-D model of S. typhi OMPLA from ProSa-web: (a) Overall model quality of template, OMPLA (PDB ID:1QD5) from E. coli (Z=-4.21); (b) Local model quality of 1QD5; (c) Overall model quality of modeled OMPLA from S. typhi (Z=-4.09); & (d) Local model quality of modeled OMPLA from S. typhi.


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inhibition. This epitope has also been found to bind selected MHC molecules (A*0201, A*0204, B*2705, and DRB1*0401) and to 99.5% HLA molecules in T-epitope designer. The LsrC protein is also antigenic and one epitope “FIPNQTGTG” from Cluster-I (Score: 13.443, Residue No: 9) was found to be antigenic (VaxiJen score: 1.2755) and can bind 7 MHC molecules of both the MHC class I and II. The IC50 value of this epitope for DRB1*0101 and DRB1*0401 was 147.23 and 389.94 nM, respectively, which indicates a good inhibition. While this epitope (FIPNQTGTG) is located outside to the cell (surface exposed), the other epitope (FYATGDNLQ) was located inside the cytoplasm of the cell. It also binds to maximum number of MHC alleles (seven alleles). This epitope has also been found to bind selected MHC molecules (B*2705, and DRB1*0401) and also bind to 75.31% HLA molecules in T-epitope designer. Final list of selected T-cell epitopes are shown in Table 4.

The DISTILL server was used to generate 3-D structure of predicted epitopes (Figs 8a & b), but both

the validation tools (ProSA-web and Procheck) show that these models are highly unusual (data not shown). Furthermore, no domain or motif could be assigned using ProFunc, Motif Scan and InterProScan for OMPLA and LsrC, the epitope from S. typhi. Calculated mol wt and pI of the epitope

Fig. 6––The Ramachandran plot for S. typhi LsrC

Fig. 7(a & b)––Topology of epitopes identified using Pepitope server: a) OMPLA protein, epitope (WQLSNSKES) represented in red color is located outside; & b) LsrC protein, epitope (FIPNQTGTG) represented in red color is located outside.

Fig. 5––The Ramachandran plot for S. typhi OMPLA


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(WQLSNSKES) from OMPLA were respectively, 1078.15 Da and 6.00, and was found to be undigested by Cyanogen bromide, Clostripain, Proline Endopept, Trypsin R and AspN, as determined by ProteinDigest. For epitope (FIPNQTGTG) from LsrC, mol wt and pI were 934.02 Da and 5.52, respectively, and was found to be undigested by Trypsin, Cyanogen bromide, Clostripain, IodosoBenzoate, Staph protease and AspN.

Common Epitope for Multiple Pathogens

From sequence (Figs 9a & b) and structure (Figs 10 a & b) based homology analyses, it was

found that epitopes identified from other pathogens like S. typhimurium, Klebsiella pneumoniae, Proteus

vulgaris, E. coli and Shigella flexneri are located at nearly same accessible region similar to OMPLA and LsrC epitopes of S. typhi. The identified epitopes from OMPLA and LsrC may induce B-cell and T-cell mediated immunity as evident from acceptable antigenic scores, binding affinities to MHC class I (A*0201, A*0204, B*2705) and IC50 values for MHC Class II (DRB1*0101, DRB1*0401) specific alleles (Tables 5 a & b). Therefore, they are also potential vaccine candidates.

Table 4––The final selected epitopes showing MHC binding and inhibition values predicted from 3-D QSAR based T-epitope Designer and MHCPred server

Protein Epitopes T-epitope Designer MHCPred (IC50 value)

A*0201 A*0204 B*2705 DRB1*0101 DRB1*0401

OMPLA WQLSNSKES 1204.43 673.98 3090.42 47.64 36.64

LsrC FIPNQTGTG -433.27 -911.71 825.79 147.23 389.94

Table 5a––Common epitopes from OMPLA and LsrC of multiple pathogens that are homologous to S. typhi OMPLA and LsrC epitopes

Proteins Pathogen Epitope from S. typhi and

homologous epitopes Sequence position VaxiJen score

S. typhi WQLSNSKES 98 1.0261 OMPLA S. typhimurium WQLSNSKES 98 1.0261 K. pneumoniae WQLSNSKES 94 1.0261 P. vulgaris WQLSNTGES 98 1.0693 E. coli WQLSNSEES 98 0.9549

S. typhi FIPNQTGTG 231 1.2755 LsrC Shigella flexneri FILNQTGTG 231 0.6907 S. typhimurium FIPNQTGTG 231 1.2755 E. coli FIPNQTGTG 231 1.2755

Table 5b—Binding affinities of common epitopes from OMPLA and LsrC of multiple pathogens against MHC class I and Class II alleles

T-epitope Designer MHCPred (IC50 value) Proteins Pathogen Epitopes

A*0201 A*0204 B*2705 DRB1*0101 DRB1*0401 S. typhi WQLSNSKES 1204.43 673.98 3090.42 47.64 36.64

OMPLA S. typhimurium WQLSNSKES 1204.43 673.98 3090.42 47.64 36.64 K. pneumoniae WQLSNSKES 1204.43 673.98 3090.42 47.64 36.64 P. vulgaris WQLSNTGES 236.09 -20.20 2147.81 55.59 56.62 S. typhi FIPNQTGTG -433.27 -911.71 825.79 147.23 389.94 Shigella flexneri FILNQTGTG -123.91 -254.93 1014.08 123.31 204.64

LsrC S. typhimurium FIPNQTGTG -433.27 -911.71 825.79 147.23 389.94 E. coli FIPNQTGTG -433.27 -911.71 825.79 147.23 389.94


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Docking and Simulation

Epitope (WQLSNSKES) from OMPLA was docked with crystal structure of DRB1*0401 (Fig. 11) showing hydrogen bond interaction between Gln99-Tyr102, Gln99-Asp181 and Ser103-Asp181. Similarly, epitope (FIPNQTGTG) from LsrC was docked with crystal structure of DRB1*0401 (Fig. 12) showing hydrogen bond interaction between Phe231-Glu47, Gln235-Glu46, Gly237-Arg44, Gly239-Asn94 and Gly239-Asp152. Molecular dynamics simulation was performed in a water environment for both the complexes. A standard way to measure the quality of simulation is to monitor the deviation from starting conformation throughout the simulation. The RMSD of simulated structure DRB1*0401-WQLSNSKES complex stabilized around 400 ps (0.869 Å) and remained stable till 800 ps (0.752 Å) (Fig. 13 b). During this time period, potential energy (–61408.9 J) also remained stable (Fig. 13a). Three hydrogen bonds were formed in the docked structure (Gln99-Tyr102, Gln99-Asp181 and Ser103-Asp181). After simulation upto 1 ns, two original bonds (Gln99-Tyr102 and Gln99-Asp181) were lost but were replaced by one new bond

Fig. 8(a & b)––3-D structure of epitopes modeled using DISTILL server: a) S. typhi OMPLA, & b) S.typhi LsrC.

Fig. 9(a & b)––Multiple sequence alignment of (a) OMPLA and (b) LsrC from other human pathogens using Clustal W (Identified epitopes are shown within box)

Fig. 10(a & b)–– Structure based superimposition of: a) S. typhi OMPLA epitope (WQLSNSKES) against various other pathogens; & b) S. typhi LsrC epitope (FIPNQTGTG) against various other pathogens.


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Fig. 11––Docked structure of epitope from OMPLA with DRB1*0401 shows hydrogen bonds between Gln99-Tyr102, Gln99-Asp181 and Ser103-Asp181

Fig. 13(a-d)––MD simulation of MHC-epitope complex upto 1 ns: Potential energy (a) and RMSD (b) plots, respectively of DRB1*0401-WQLSNSKES (OMPLA) complex; Potential energy (c) and RMSD (d) plots, respectively of DRB1*0401-FIPNQTGTG (LsrC) complex. [X-axis represents time (scale: 20 =200 Pico seconds) and Y-axis represents potential energy (a, c) or RMSD (b, d) values]

Fig. 12––Docked structure of epitope from LsrC with DRB1*0401 shows hydrogen bonds between Phe231-Glu47, Gln235-Glu46, Gly237-Arg44, Gly239-Asn94 and Gly239-Asp152


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(Trp98-Thr93), while the other one (Ser103-Asp181) remained stable throughout the simulation (Table 6). The RMSD of simulated structure DRB1*0401-FIPNQTGTG complex was stabilized around 500 ps (0.831 Å) and remained stable till 800 ps (0.732 Å) (Fig. 13d). During this time period, potential energy (–60767.8 J) also remained stable (Fig. 13c). Five hydrogen bonds were formed in the docked structure (Phe231-Glu47, Gln235-Glu46, Gly237-Arg44, Gly239-Asn94 and Gly239-Asp152). After simulation upto 1 ns, two original bonds (Gly239-Asn94 and Gly239-Asp152) were lost, but they were replaced by two new bonds (Thr238-Asn94 and Thr238-Asp152), while the other three (Phe231-Glu47, Gln235-Glu46 and Gly237-Arg44) remained stable throughout the simulation (Table 7).

Conclusion In the present study, both B-cell and T-cell epitopes from OMPLA and LsrC were identified. In simulation studies, MHC-epitope complexes were found to be flexible and remained stable upto 1 ns. These epitopes were able to induce both the B-cell and T-cell mediated immune responses. So these two epitopes (98WQLSNSKES106 & 231FIPNQTGTG239) can be considered as good peptide-based subunit vaccine candidates. They can also be used in developing a

vaccine against all other human pathogens like S. typhimurium, K. pneumoniae, P. vulgaris and S. flexneri. The identified epitopes require proper experimental validation for their use as an effective vaccine against these human pathogens.

Acknowledgement Authors are thankful to Dr N Singaravelan, Department of Biology, Institute of Evolution, Haifa University, Israel for his valuable comments on the manuscript and to Mr E Murugesh, Department of Bioinformatics, Bharathiar University, Coimbatore for his inputs during MD simulations. References 1 Germanier R, Bacterial vaccines (Academic Press, London)

1984, 48-49. 2 Crump J A, Luby S P & Mintz E D, The Global burden of

typhoid fever, Bull World Health Organ, 82 (2004) 346-353. 3 Henke J M & Bassler B L, Bacterial social engagements,

Trends Cell Biol, 14 (2004) 648-656. 4 Miller M B & Bassler B L, Quorum sensing in bacteria,

Annu Rev Microbiol, 55 (2001) 165-199. 5 Waters C M & Bassler B L Quorum sensing: Cell-to-cell

communication in bacteria, Annu Rev Cell Dev Biol, 21

(2005) 319-346. 6 Bassler B L & Losick R, Bacterially speaking, Cell, 125

(2006) 237-246. 7 Taga M E, Semmelhack J L & Bassler B L, The LuxS-

dependent autoinducer AI-2 controls the expression of an

Table 6––Hydrogen bonds have been analyzed for DRB1*0401-WQLSNSKES (OMPLA) complex during 1 ns simulation for every 100 ps (Time averaged hydrogen bond distance between donor-acceptor is less than 3.5 Å)

Epitope DRB1*0401 0ps 100ps 200ps 300ps 400ps 500ps 600ps 700ps 800ps 900ps 1000ps

Trp98 Thr93 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Tyr102 ✓ ✓ ✓ Gln99 Asp181 ✓

Asn102 Ala104 ✓ ✓ ✓ ✓ Ser103 Asp181 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Ser106 Asp181 ✓

Table 7––Hydrogen bonds have been analyzed for DRB1*0401-FIPNQTGTG (LsrC) complex during 1 ns simulation for every 100 ps (Time averaged hydrogen bond distance between donor-acceptor is less than 3.5 Å)

Epitope DRB1*0401 0ps 100ps 200ps 300ps 400ps 500ps 600ps 700ps 800ps 900ps 1000ps

Phe231 Glu47 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Gln235 Glu46 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Thr236 Ser133 ✓ ✓ Gly237 Arg44 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

Asn94 ✓ ✓ ✓ ✓ ✓ Thr238 Asp152 ✓ ✓ ✓ ✓ ✓ ✓ ✓

Asn94 ✓ ✓ ✓ ✓ ✓ ✓ Gly239 Asp152 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓


451

ABC transporter that functions in AI-2 uptake in Salmonella

typhimurium, Mol Microbiol, 42 (2001) 777-793. 8 Ada G, The importance of vaccination, Front Biosci, 12

(2007) 1278-1290. 9 Chandra S, Singh D & Singh T R, Prediction and

characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitides serogroup B, Bioinformation, 5 (2010) 155-167.

10 Van Hoff D D, Evans D B & Hruban R H, Pancreatic cancer (Jones & Bartlett Publishers, Massachusetts) 2005, 188.

11 Cerdino-tarraga A M, Efstratiou A L, Dover G, Holden M T G, Pallen M et al, The complete genome sequence and analysis of Corynebacterium diphtheriae NCTC13129, Nucleic Acids Res, 31 (2003) 6516.

12 Disis M L, Gralow J R, Bernhard H, Hand S L, Rubin W D et al, Peptide-based, but not whole protein, vaccines elicit immunity to HER- 2/neu, oncogenic self-protein, J Immunol, 9 (1996) 3151-3158.

13 Florea L, Halldórsson B, Kohlbacher O, Schwartz R, Hoffman S et al, Epitope prediction algorithms for peptide-based vaccine design, Proc Comput Soc Bioinform, 2 (2003) 17-26.

14 Watts C, Capture and processing of exogenous antigens for presentation on MHC molecules, Annu Rev Immunol, 15 (1997) 821.

15 Germain R N, MHC-dependent antigen processing and peptide presentation: Providing ligands for T lymphocyte activation, Cell, 76 (1994) 287.

16 Singh H & Raghava G P S, ProPred: Prediction of HLA-DR binding sites, Bioinformatics, 17 (2001) 1236-1237.

17 Toes R E, Nussbaum A K, Degermann S, Schirle M, Emmerich N P N et al, Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products, J Exp Med, 194 (2001) 1-12.

18 DeRoeck D, Jodar L & Clemens J, Putting typhoid vaccination on the global health agenda, N Engl J Med, 357 (2007) 1069-1071.

19 Doytchinova I A & Flower D R, VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines, BMC Bioinformatics, 8 (2007) 4.

20 El-Manzalawy Y, Dobbs D & Honavar V, Predicting linear B-cell epitopes using string kernels, J Mol Recognit, 21 (2008) 243-255.

21 Chen J, Liu H, Yang J & Chou K C, Prediction of linear B-cell epitopes using amino acid pair antigenicity scale, Amino

Acids, 33 (2007) 423-428. 22 Singh H & Raghava G P, ProPred1: Prediction of

promiscuous MHC Class-I binding sites, Bioinformatics, 19 (2003) 1009-1014.

23 Guan P, Doytchinova I A, Zygouri C & Flower D R, MHCPred: A server for quantitative prediction of peptide-MHC binding, Nucleic Acids Res, 31 (2003) 3621-3624.

24 Kangueane P & Sakharkar M K, T-Epitope Designer: A HLA-peptide binding prediction server, Bioinformation, 1 (2005) 21.

25 Sali A & Blundell T L, Comparative protein modeling by satisfaction of spatial restraints, J Mol Biol, 234 (1993) 779-815.

26 Zhang Y, I-TASSER: Fully automated protein structure prediction in CASP8, Proteins, S9 (2009) 100-113.

27 Wiederstein M & Sippl M J, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins, Nucleic Acids Res, 35 (2007) 407-410.

28 Laskowski R A, MacArthur M W, Moss D S & Thornton J M, PROCHECK—A program to check the stereochemical quality of protein structures, J Appl Cryst, 26 (1993) 283-291.

29 Mayrose I, Penn O, Erez E, Rubinstein N D, Shlomi T et al, Pepitope: Epitope mapping from affinity-selected peptides, Bioinformatics, 23 (2007) 3244.

30 Mayrose I, Shlomi T, Rubinstein N D, Gershoni J M, Ruppin E et al, Epitope mapping using combinatorial phage-display libraries: A graph-based algorithm, Nucleic Acids Res, 35 (2007) 69-78.

31 Bublil E M, Freund N T, Mayrose I, Penn O, Roitburd-Berman A et al, Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm, Proteins, 68 (2007) 294-304.

32 Bau D, Martin A J, Mooney C, Vullo A, Walsh I et al, Distill: A suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins, BMC

Bioinformatics, 7 (2006) 402. 33 Laskowski R A, Watson J D & Thornton J M, ProFunc: A

server for predicting protein function from 3-D structure, Nucleic Acids Res, 33 (2005) 89-93.

34 Friesner R A, Banks J L, Murphy R B, Halgren T A, Klicic J J et al, Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy, J Med Chem, 47 (2004) 1739-1749.

35 Salameh B A, Cumpstey I, Sundin A, Leffler, H & Nilsson U J, 1H-1,2,3-Triazol-1-yl thiodigalactoside derivatives as high affinity galectin-3 inhibitors, Bioorg Med Chem, 18 (2010) 5367-5378.

36 Grant K A, Ubarretxena-Belandia I, Dekker N, Richardson P T & Park S F, Molecular characterization of pldA, the structural gene for a phospholipase A from Campylobacter

coli, and its contribution to cell-associated hemolysis, Infect

Immun, 65 (1997) 1172-1180. 37 Dorrell N, Martino M C, Stabler R A, Ward S J, Zhang Z W

et al, Characterization of Helicobacter pylori PldA, a phospholipase with a role in colonization of the gastric mucosa, Gastroenterology, 117 (1999) 1098-1104.

38 Larsen J E, Lund O & Nielsen M, Improved method for predicting linear B-cell epitopes, Immunome Res, 2 (2006) 2.

39 Wiwanitkit V, Predicted epitopes of malarial merozoite surface protein 1 by bioinformatics method: A clue for further vaccine development, J Microbiol Immunol Infec, 42 (2009) 19-21.

40 Wang B, Yao K, Liu G, Xie F, Zhou F et al, Computational prediction and identification of Epstein-Barr virus latent membrane protein 2A antigen-specific CD8+ T-cell epitopes, Cell Mol Immunol, 6 (2009) 97-103.

41 Dunstan S J, Stephens H A , Blackwell J M, Duc C M, Lanh M N et al, Genes of the class II and class III major histocompatibility complex are associated with typhoid fever in Vietnam, J Infect Dis, 183 (2001) 261-268.

In silico identification of B- and T-cell epitopes on OMPLA and...

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