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Immunological feature predictions and databases on the web

Immunological feature predictions and databases on the web. Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark lund@cbs.dtu.dk. Effect of vaccines. Vaccines have been made for 36 of >400 human pathogens. +HPV & Rotavirus.

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Immunological feature predictions and databases on the web

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  1. Immunological feature predictions and databases on the web Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark lund@cbs.dtu.dk

  2. Effect of vaccines

  3. Vaccines have been made for 36 of >400 human pathogens +HPV & Rotavirus Immunological Bioinformatics, The MIT press.

  4. Deaths from infectious diseases in the world in 2002 www.who.int/entity/whr/2004/annex/topic/en/annex_2_en.pdf

  5. Pathogenic Viruses 1st column: log10 of the number of deaths caused by the pathogen per year2nd 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 threats4th 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 Data derived from /www.cbs.dtu.dk/databases/Dodo.

  6. Need for new vaccine technologies • The classical way of making vaccines have in many cases been tried for the pathogens for which no vaccines exist • Need for new ways for making vaccines

  7. Databases Used for Vaccine Design • Sequence databases • General • Sequences of proteins of the immune system • Epitope databases • Pathogen centered databases • HIV • mTB • Malaria

  8. Sequence Databases • Used to study sequence variability of microbes • Sequence conservation • Positive/negative selection • Examples • Swissprot http://expasy.org/sprot/ • GenBank http://www.ncbi.nlm.nih.gov/Genbank/

  9. MHC Class I pathway Figure by Eric A.J. Reits

  10. The binding of an immunodominant 9-mer Vaccinia CTL epitope, HRP2 (KVDDTFYYV) to HLA-A*0201. Position 2 and 9 of the epitopes are buried deeply in the HLA class I molecule. Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004).

  11. Expression of HLA is codominant

  12. Polymorphism and polygeny

  13. The MHC gene region http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init&user_id=0&probe_id=0&source_id=0&locus_id=0&locus_group=0&proto_id=0&banner=1&kit_id=0&graphview=0

  14. Human Leukocyte antigen (HLA=MHC in humans) polymorphism - alleles http://www.anthonynolan.com/HIG/index.html

  15. HLA variability http://rheumb.bham.ac.uk/teaching/immunology/tutorials/mhc%20polymorphism.jpg

  16. Logos of HLA-A alleles O Lund et al., Immunogenetics. 2004 55:797-810

  17. Clustering of HLA alleles O Lund et al., Immunogenetics. 2004 55:797-810

  18. Databases of Sequences of Proteins of Immune system • Used to study variability of the human genome • IMmunoGeneTics HLA (IMGT/HLA) database • Sequences of HLA, antibody and other molecules • http://imgt.cines.fr/ • dbMHC • Clinical data and sequences related to the immune system • http://www.ncbi.nlm.nih.gov/mhc/MHC.fcgi?cmd=init • Anthony Nolan Database • http://www.anthonynolan.com/HIG/

  19. Epitope Databases • Used to find regions that can be recognized by the immune system • General Epitope Databases • IEDB General epitope database • http://immuneepitope.org/home.do • AntiJen (MHC Ligand, TCR-MHC Complexes, T Cell Epitope, TAP , B Cell Epitope molecules and immunological Protein-Protein interactions) • http://www.jenner.ac.uk/AntiJen/ • FIMM (MHC, antigens, epitopes, and diseases) • http://research.i2r.a-star.edu.sg/fimm/

  20. More Epitope Databases • SYFPEITHI • Natural ligands: sequences of peptides eluded from MHC molecules on the surface of cells • http://www.syfpeithi.de/ • MHCBN: Immune related databases and predictors • http://www.imtech.res.in/raghava/mhcbn/ • http://bioinformatics.uams.edu/mirror/mhcbn/ • HLA Ligand/Motif Database: Discontinued • MHCPep: Static since 1998, replaced by FIMM

  21. Prediction of HLA binding • Many methods available, including: • bimas, syfpeithi, Hlaligand, libscore, mapppB, mapppS,mhcpred, netmhc, pepdist, predbalbc, predep, rankpep, svmhc • See links at: • http://immuneepitope.org/hyperlinks.do?dispatch=loadLinks • Recent benchmark: • http://mhcbindingpredictions.immuneepitope.org/internal_allele.html

  22. B cell Epitope Databases • Linear • IEDB, Bcipep, Jenner, FIMM, BepiPred • HIV specific database • http://www.hiv.lanl.gov/content/immunology/ab_search • Conformational • CED: Conformational B cell epitopes • http://web.kuicr.kyoto-u.ac.jp/~ced/

  23. MHC class II pathway Figure by Eric A.J. Reits

  24. Virtual matrices • HLA-DR molecules sharing the same pocket amino acid pattern, are assumed to have identical amino acid binding preferences.

  25. MHC Class II binding • Virtual matrices • TEPITOPE: Hammer, J., Current Opinion in Immunology 7, 263-269, 1995, • PROPRED: Singh H, Raghava GPBioinformatics 2001 Dec;17(12):1236-7 • Web interface http://www.imtech.res.in/raghava/propred

  26. MHC class II Supertypes • 5 alleles from the DQ locus (DQ1, DQ2, DQ3, DQ4, DQ5) cover 95% of most populations [Gulukota and DeLisi, 1996] • A number of HLA-DR types share overlapping peptide-binding repertoires [Southwood et al., 1998]

  27. Logos of HLA-DR alleles O Lund et al., Immunogenetics. 2004 55:797-810

  28. O Lund et al., Immunogenetics. 2004 55:797-810

  29. Linear B cell Epitope Predictors • Continuous (Linear) epitopes • IEDB • http://tools.immuneepitope.org/tools/bcell/iedb_input • Bcepred • www.imtech.res.in/raghava/btxpred/link.html • Bepipred • http://www.cbs.dtu.dk/services/BepiPred/ • Recent Benchmarking Publications • Benchmarking B cell epitope prediction: Underperformance of existing methods. Blythe MJ, Flower DR. Protein Sci. 2005 14:246-24 • Improved method for predicting linear B-cell epitopes Jens Erik Pontoppidan Larsen, Ole Lund and Morten Nielsen Immunome Research 2:2, 2006 • Greenbaum JA, Andersen PH, Blythe M, Bui HH, Cachau RE, Crowe J, Davies M, Kolaskar AS, Lund O, Morrison S, Mumey B, Ofran Y, Pellequer JL, Pinilla C, Ponomarenko JV, Raghava GP, van Regenmortel MH, Roggen EL, Sette A, Schlessinger A, Sollner J, Zand M, Peters B. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. J Mol Recognit. 2007 Jan 5

  30. Discontinuous B cell Epitope Predictors • Discontinuous (conformational) epitopes • DiscoTope • http://www.cbs.dtu.dk/services/DiscoTope/ • Benchmarking • Prediction of residues in discontinuous B cell epitopes using protein 3D structures, Pernille Haste Andersen, Morten Nielsen and Ole Lund, Protein Science, 15:2558-2567, 2006

  31. Pathogen Centered Databases • HIV • http://www.hiv.lanl.gov/content/index • Influenza • http://www.flu.lanl.gov/ • Tuberculosis • http://www.sanger.ac.uk/Projects/M_tuberculosis/ • POX • http://www.poxvirus.org/

  32. Reviews • Tong JC, Tan TW, Ranganathan S. Methods and protocols for prediction of immunogenic epitopes. Brief Bioinform. 2006 Oct 31 • Web based Tools for Vaccine Design (Lund et al, 2002) • http://www.cbs.dtu.dk/researchgroups/immunology/webreview.html

  33. Other Resources • Gene expression data • Localization prediction • SignalP

  34. Other BioTools at CBS • Mapping of epitopes from multiple strains on one reference sequence • Training matrix and neural network methods • Training of Gibbs sampler

  35. Future challenges • Consensus on benchmarks • Like Rost-Sander set in secondary structure prediction • …but more complicated • Different types of epitopes • B cell , T cell (Class I and II) • Different validation experiments • HLA binders, natural ligands, epitopes • Linear and conformational B cell epitopes • Many alleles

  36. Links to links • IEDB’s Links • http://immuneepitope.org/hyperlinks.do?dispatch=loadLinks

  37. Epitope Discovery

  38. Peptide-MHC complex Incubation Development Determination of peptide-HLA binding • Step I: Folding of MHC class I molecules in solution b2m Heavy chain peptide • Step II: Detection of de novo folded MHC class I molecules by ELISA C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8

  39. HLA Binding Results KD\PathogenInfluenza Marburg Pox F. tularensis Dengue Hantaan Lassa West Nile Yellow Fever KD<50 42 45 97 45 67 59 27 52 50 50<KD<500 63 39 42 21 44 20 21 41 52 KD>500 87 29 38 6 30 11 22 29 35 in progress 9 1 1 4 6 4 12 31 33 Total 201 114 178 76 147 94 82 153 170 • 1215 peptides received • 1114 tested for binding • 827 (74%) bind with KD better than 500nM • 484 (43%) bind with KD better han 50 nM Søren Buus Lab

  40. ELISPOT assay • Measure number of white blood cells that in vitro produce interferon-g in response to a peptide • A positive result means that the immune system has earlier reacted to the peptide (during a response to a vaccine/natural infection) SLFNTVATL SLFNTVATL SLFNTVATL SLFNTVATL SLFNTVATL SLFNTVATL Two spots

  41. Influenza Peptides positive in ELISPOT Mingjun Wang et al., submitted

  42. Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3:e91.

  43. Genome Projects -> Systems Biology • Genome projects • Create list of components • Sequence genomes • Find genes • Systems Biology • Find out how these components play together • Networks of interactions • Simulation of systems • Over time • In 3D space

  44. Simulation of the Immune system

  45. Example • CTL escape mutant dynamics during HIV infection Ilka Hoof and Nicolas Rapin

  46. Flowchart - interactions Nicolas Rapin et al., Journal of Biological Physics, In press

  47. Mathematical model Nicolas Rapin

  48. f values from sequence Sequence f value -------------------- SLYNTVATL 1 SAYNTVATL 0.95283 SAYNTVATC 0.90566 SAFNTVATC 0.86792 SAINTVATC 0.83019 VAINTVATC 0.77358 VAINTHATC 0.70755 VAINEHATC 0.65094 VAICEHATC 0.56604 VAICEPATC 0.57547

  49. From one to many virus strains

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