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

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

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

6. Pathogenic 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) classification DNA 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 into four classes. Risk group 1 (RG1). Agents that are not associated with disease in healthy adult humans Risk group 2 (RG2). Agents that are associated with human disease which is rarely serious and for which preventive or therapeutic interventions are often available Risk 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 classification classification 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 available A 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

7. 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

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

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

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

11. 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).

12. Expression of HLA is codominant

13. Polymorphism and polygeny

14. 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

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

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

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

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

19. 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/

20. 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/

21. 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

22. 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=load Linkshttp://immuneepitope.org/hyperlinks.do?dispatch=load Links Recent benchmark: http://mhcbindingpredictions.immuneepitope.org/intern al_allele.htmlhttp://mhcbindingpredictions.immuneepitope.org/intern al_allele.html

23. 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/

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

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

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

27. 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]

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

30. 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

31. 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

32. 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/

33. 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.htmlhttp://www.cbs.dtu.dk/researchgroups/immunology/ webreview.html

34. Other Resources Gene expression data Localization prediction SignalP

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

36. 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

37. Links to links IEDB’s Links http://immuneepitope.org/hyperlinks.do?dispatch=load Links

38. PathogenBindELISPOT InfluenzaXX W Hildebrand Variola major (smallpox) vaccineXX R Koup, S Joyce Yersinia pestisX Francisella tularensis (tularemia)X(X) A Sjostedt LCMX Lassa FeverX(x) A Edelstein, J Botton Hantaan virus (Korean hemorrhagic fever virus)X(x) A Edelstein, J Botton Rift Valley FeverX DengueX(X) E Marques EbolaX MarburgX Multi-drug resistant TB (BCG vaccine)XX Yellow feverX(X) T August Typhus fever (Rickettsia prowazekii)X(x) S Miguel West Nile VirusX(X) P Norris Epitope Discovery

39. Development 2m2m2m2m Heavy chain peptide Incubation Peptide-MHC complex Determination of peptide-HLA binding Step I: Folding of MHC class I molecules in solution Step II: Detection of de novo folded MHC class I molecules by ELISA C Sylvester-Hvid et al., Tissue Antigens. 2002 59:251-8

40. HLA Binding Results 1215 peptides received 1114 tested for binding 827 (74%) bind with K D better than 500nM 484 (43%) bind with K D better han 50 nM KD\PathogenInfluenzaMarburgPoxF. tularensisDengueHantaanLassaWest NileYellow Fever K D <50424597456759275250 50<K D <500 633942214420214152 K D >50087293863011222935 in progress911464123133 Total201114178761479482153170 Søren Buus Lab

41. ELISPOT assay Measure number of white blood cells that in vitro produce interferon-  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 Two spots

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

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

44. 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

45. Simulation of the Immune system

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

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

48. Mathematical model Nicolas Rapin

49. 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

50. From one to many virus strains

51. Nicolas Rapin Simulation with many viruses

52. HIV evolution tree. Initial virus is SLYNTVATL, that give rise to 6 functional mutants able to replicate.

53. Eleonora Kulberkyte

54. Acknowledgements Immunological Bioinformatics group, CBS, Technical University of Denmark (www.cbs.dtu.dk) Claus Lundegaard Data bases, HLA binding Morten Nielsen HLA binding Jean Vennestrøm 2D proteomics Thomas Blicher (50%) MHC structure Mette Voldby Larsen Phd student - CTL prediction Pernille Haste Andersen PhD student – Structure Sune Frankild PhD student - Databases Sheila Tuyet Tang Pox/TB Thomas Rask (50%) Evolution Ilka Hoof and Nicolas Rapin Simulation of the immune system Hao Zhang Protein potentials Collaborators IMMI, University of Copenhagen Søren BuusMHC binding Mogens H ClaessonElispot Assay La Jolla Institute of Allergy and Infectious Diseases Allesandro SetteEpitope database Bjoern Peters Leiden University Medical Center Tom OttenhoffTuberculosis Michel Klein Ganymed Ugur SahinGenetic library University of Tubingen Stefan StevanovicMHC ligands INSERM Peter van EndertTap binding University of Mainz Hansjörg SchildProteasome Schafer-Nielsen Claus Schafer-NielsenPeptide synthesis ImmunoGrid Elda Rossi&Simulation of the PartnersImmune system University of Utrectht Can KesmirIdeas