Immunological Bioinformatics
Ole LundCenter for Biological Sequence Analysis
BioCentrum-DTUTechnical University of Denmark
Challenges and failures of the immune system
Outside
Infection with microbe A
Infection with microbe B
Allergen -> allergy
Peptide drugs
Time
Creation of self
Creation of an immune system/
Tolerance to self
Autoimmunity
(break of tolerance to self)
Cancer
Inside
Basic concepts
•Immue: Free form disease (from greek)
Antigen: Something that can generate a response by the immune system
Immune system: Something that reacts to an antigen
Clonal selection theory
Figure from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Search&db=books&doptcmdl=GenBookHL&term=clonal+selection+AND+imm%5Bbook%5D+AND+125019%5Buid%5D&rid=imm.figgrp.59
•Antigen: Something that can generate an immune response
Immune system: Something that reacts to an antigen
Memory
Figure from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Search&db=books&doptcmdl=GenBookHL&term=typical+antibody+response+AND+imm%5Bbook%5D+AND+125025%5Buid%5D&rid=imm.figgrp.70
Eradication of smallpox
Figure from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Search&db=books&doptcmdl=GenBookHL&term=eradication+of+smallpox+AND+imm%5Bbook%5D+AND+125001%5Buid%5D&rid=imm.figgrp.37
How does the immune system “see” a virus?
Immune system overview•Innate – fast, unspecific
•Addaptive – specific, remembers…
•Cellular
•Cytotoxic T lymphocytes (CTL)
•Helper T lymphocytes (HTL)
•Humoral
•B lymphocytes
Figures by Eric A.J. Reits
MHC Class I pathway
1:5
1:200 1:2
Response to 1:(5x20x200) = 1:2000 9mers
Figure by Eric A.J. Reits
Use of bioinformatics
Lauemøller et al., 2000
Figure by Thomas Blicher
Peptide
T Cell receptor (TCR)
MHC
β2mCD8
Antigen Presenting cell
Cytotoxic T Lymphocyte
Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004).
Influenza A virus (A/Goose/Guangdong/1/96(H5N1))
>polymerase
MERIKELRDLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPALRMKWMMAMKYPITAD
KRIMEMIPERNEQGQTLWSKTNDAGSDRVMVSPLAVTWWNRNGPTTSTVHYPKVYKTYFE
KVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSE
SQLTITKEKKEELQDCKIAPLMVAYMLERELVRKTRFLPVAGGTSSVYIEVLHLTQGTCW
EQMYTPGGEVRNDDVDQSLIIAARNIVRRATVSADPLASLLEMCHSTQIGGIRMVDILRQ
NPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTNGSSVKKEEEVLTGNLQTLKIKVHEGY
EEFTMVGRRATAILRKATRRLIQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNF
...
and 9 other proteins
MERIKELRD
ERIKELRDL
RIKELRDLM
IKELRDLMS
KELRDLMSQ
ELRDLMSQS
LRDLMSQSR
RDLMSQSRT
DLMSQSRTR
LMSQSRTRE
and 4376 other 9mers
Proteins 9mer peptides
>Segment 1
agcaaaagcaggtcaattatattcaatatggaaagaataaaagaactaagagatctaatg
tcgcagtcccgcactcgcgagatactaacaaaaaccactgtggatcatatggccataatc
aagaaatacacatcaggaagacaagagaagaaccctgctctcagaatgaaatggatgatg
gcaatgaaatatccaatcacagcagacaagagaataatggagatgattcctgaaaggaat
and 13350 other nucleotides on 8 segments
Genome
Weight matrices (Hidden Markov models)
YMNGTMSQVGILGFVFTLALWGFFPVVILKEPVHGVILGFVFTLTLLFGYPVYVGLSPTVWLSWLSLLVPFVFLPSDFFPSCVGGLLTMVFIAGNSAYE
A2 Logo
A
FC
G
Lauemøller et al., 2000
Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles
http://www.anthonynolan.com/HIG/index.html
HLA polymorphism - supertypes
•Each HLA molecule within a supertypeessentially binds the same peptides•Nine major HLA class I supertypes have been defined
•HLA-A1, A2, A3, A24,B7, B27, B44, B58, B62
Sette et al, Immunogenetics (1999) 50:201-212
HLA polymorphism - frequencies
Supertypes Phenotype frequenciesCaucasian Black Japanese Chinese Hispanic Average
A2,A3, B7 83 % 86 % 88 % 88 % 86 % 86%
+A1, A24, B44 100 % 98 % 100 % 100 % 99 % 99 %
+B27, B58, B62 100 % 100 % 100 % 100 % 100 % 100 %
A Sette et al, Immunogenetics (1999) 50:201-212
O Lund et al., Immunogenetics. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
O Lund et al., Immunogenetics. 2004 55:797-810
Combined method
•Combining predicted MHC-I affinity with prediction of C-terminal proteasomal cleavage and TAP transport efficiency improves the ability to identify known CTL epitopes
MV Larsen et al., Accepted for publication in European Journal of Immunology
Infectious Diseases
•More than 400 microbial agents are associated with disease in healthy adult humans
•There are only licensed vaccines in the United states for 22 microbial agents (vaccines for 34 pathogens have been developed)
•Immunological Bioinformatics may be used to
•Identify immunogenic regions in pathogen
•These regions may be used as in rational vaccine design
•Which pathogens to focus on? Infectious diseases may be ranked based on
•Impact on health
•Dangerousness
•Economic impact
Infectious Diseases in the World
•11 million (19%) of the 57 million people who died in the world in 2002 were killed by infectious or parasitic infection [WHO, 2004]
•The three main single infectious diseases are HIV/AIDS, tuberculosis, and malaria, each of which causes more than 1 million deaths
Deaths from infectious diseases in the world in 2002
www.who.int/entity/whr/2004/annex/topic/en/annex_2_en.pdf
DodoPathogenic Viruses
Data derived from /www.cbs.dtu.dk/databases/Dodo.
1st column: log10 of the number of deaths caused by the pathogen per year
2nd column: DNA Advisory Committee (RAC) classificationDNA Advisory Committee guidelines [RAC, 2002] which includes those biological agents known to infect humans, as well as selected animal agents that may pose theoretical risks if inoculated into humans. RAC divides pathogens intofour classes.Risk group 1 (RG1). Agents that are not associated with disease in healthy adult humansRisk group 2 (RG2). Agents that are associated with human disease which is rarely serious and for which preventive or therapeutic interventions are often availableRisk group 3 (RG3). Agents that are associated with serious or lethal human disease for which preventive or therapeutic interventions may be available (high individual risk but low community risk)Risk group 4 (RG4). Agents that are likely to cause serious or lethal human disease for which preventive or therapeutic interventions are not usually available (high individual risk and high community risk)
3rd column: CDC/NIAID bioterror classificationclassification of the pathogens according to the Centers for Disease Control and Prevention (CDC) bioterror categories A–C, where category A pathogens are considered the worst bioterror threats
4th column: Vaccines availableA letter indicating the type of vaccine if one is available (A: acellular/adsorbet; C: conjugate; I: inactivated; L: live; P: polysaccharide; R: recombinant; S staphage lysate; T: toxoid). Lower case indicates that the vaccine is released as an investigational new drug (IND)).
5th column: G: Complete genome is sequenced
BiodefenceTargets
www2.niaid.nih.gov/Biodefense/bandc_priority.htm
NIH projectPathogen HLA binding ElispotInfluenza X XVariola major (smallpox) vaccine strain X XYersinia pestis XFrancisella tularensis (tularemia) X CBS/panumLCM XLassa Fever XHantaan virus (Korean hemorrhagic fever virus) XRift Valley Fever XDengue XEbola XMarburg XMulti-drug resistant TB (BCG vaccine) X XYellow fever XTyphus fever (Rickettsia prowazekii) XWest Nile Virus X
Strategy for determination of peptide-HLA binding
DevelopmentDevelopment
ββ22mmHeavy chainHeavy chain
peptidepeptide IncubationIncubationPeptidePeptide--MHC MHC complexcomplex
Step I: Folding of MHC class I molecules in solution
Step II: Detection of Step II: Detection of de novode novo folded MHC class I molecules by ELISAfolded MHC class I molecules by ELISA
C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8
ELISPOT assay
•Measure number of white blood cells that in vitro produce interferon-γ in response to a peptide
•A positive result means that the immune system have earlier reacted to the peptide (during a response ot a vaccine/natural infection)
SLFNTVATL
SLFNTVATL
SLFNTVATL
SLFNTVATL SLFNTVATLSLFNTVATL
Two spots
Preliminary results
•167 peptides have so far been tested for binding to a HLA molecule
•113 of these (67%) have been shown to bind to the relevant HLA allele with a affinity better than 500nM
•180 predicted epitopes from influenza A virus were tested in an ELISPOT assay
•12 were so far found to be epitopes (recognized by donors previously exposed to Influenza)
•14% of peptides binding with an affinity better than 500nM were found to be epitopes
•1:2000 randomly chosen peptides are epitopes
Vaccination
•Vaccination
•Administration of a substance to a person with the purpose of preventing a disease
•Traditionally composed of a killed or weakened microorganism
•Vaccination works by creating a type of immune response that enables the memory cells to later respond to a similar organism before it can cause disease
Early History of Vaccination•Pioneered India and China in the 17th century
•The tradition of vaccination may have originated in India in AD 1000
•Powdered scabs from people infected with smallpox was used to protect against the disease
•Smallpox was responsible for 8 to 20% of all deaths in several European countries in the 18th century
•In 1721 Lady Mary Wortley Montagu brought the knowledge of these techniques from Constantinople (now Istanbul) to England
•Two to three percent of the smallpox vaccinees, however, died from the vaccination itself
•Benjamin Jesty and, later, Edward Jenner could show that vaccination with the less dangerous cowpox could protect against infection with smallpox
•The word vaccination, which is derived from vacca, the Latin word for cow.
Successful vaccination campaigns
Figure from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Search&db=books&doptcmdl=GenBookHL&term=sucessfull+vaccines+AND+imm%5Bbook%5D+AND+125049%5Buid%5D&rid=imm.figgrp.93
Vaccination Today•Vaccines have been made for only 34 of the more than 400 known pathogens that are harmful to man.
•Immunization saves the lives of 3 million children each year, but that 2 million more lives could be saved if existing vaccines were applied on a full-scale worldwide
Human Vaccines against pathogens
Immunological Bioinformatics, The MIT press.
Categories of Vaccines•Live vaccines
•Are able to replicate in the host
•Attenuated (weakened) so they do not cause disease
•Subunit vaccines
•Part of organism
•Genetic Vaccines
•Part of genes from organism
Polytope construction
Linker
MNH2 COOH
Epitope C-terminal cleavage
Cleavage within epitopesNew epitopes
cleavage
Helper responses
Figures by Eric A.J. Reits
Figure by Anne Mølgaard
MHC class II prediction
RFFGGDRGAPKRGYLDPLIRGLLARPAKLQVKPGQPPRLLIYDASNRATGIPAGSLFVYNITTNKYKAFLDKQSALLSSDITASVNCAKPKYVHQNTLKLATGFKGEQGPKGEPDVFKELKVHHANENISRYWAIRTRSGGITYSTNEIDLQLSQEDGQTIE
Complexity of problem– Peptides of different
length– Weak motif signal
Alignment crucialGibbs Monte Carlo
sampler
M Nielsen et al., Bioinformatics. 2004 20:1388-97
Class II binding motif
RFFGGDRGAPKRGYLDPLIRGLLARPAKLQV
KPGQPPRLLIYDASNRATGIPAGSLFVYNITTNKYKAFLDKQ
SALLSSDITASVNCAKPKYVHQNTLKLAT
GFKGEQGPKGEPDVFKELKVHHANENISRYWAIRTRSGGI
TYSTNEIDLQLSQEDGQTI
RandomAlignment by Gibbs sampler ClustalW
Gibbs sampler
M Nielsen et al., Bioinformatics. 2004 20:1388-97
Antibody responses
Figures by Eric A.J. Reits
Prediction of Antibody epitopesLinear
– Hydrophilicity scales (average in ~7 window)• Hoop and Woods (1981)• Kyte and Doolittle (1982)• Parker et al. (1986)
– Other scales & combinations• Pellequer and van Regenmortel• Alix
– New improved method (Pontoppidan et al. in preparation)• http://www.cbs.dtu.dk/services/BepiPred/
Discontinuous– Protrusion (Novotny, Thornton, 1986)
• Pernille Haste Andersen, 2005, in preparation
Immunological bioinformatics
Classical experimental research– Few data points– Data recorded by pencil and
paper/spreadsheetNew experimental methods
– Sequencing– DNA arrays– Proteomics
Need to develop new methods for handling these large data sets
• Immunological Bioinformatics/Immunoinformatics
PernilleHasteAndersen
Morten Nielsen
Anne Mølgaard
SuneFrankild
ThomasBlicher
ClausLundegaard
Xiuxiu Ye
JensPontoppidan
Immunology group at CBS
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