1. Immunological Bioinformatics Ole Lund

2. Challenges of the immune system Time Creation of self Creation of an immune system/ Tolerance to self Infection with microbe A Autoimmunity (break of tolerance to self) Allergen -> allergy Vaccine Cancer Inside Outside Peptide drugs Infection with microbe B Transplant ations

3. Effect of vaccines

6. The Immune System The innate immune system The adaptive immune system

7. Adaptive immune response Signal induced –Pathogens Antigens –Epitopes B Cell T Cell

8. Presentation of peptides on MHC I Figure by Eric A.J. Reits 1:5 1:200 1:2 Response to1:(5x20x20 0) = 1:2000 peptides

9. Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004). Peptide (epitope) bound to MHC

10. Data driven predictions List of peptides that have a given biological feature Mathematical model (neural network, hidden Markov model) Search databases for other biological sequences with the same feature/property YMNGTMSQV GILGFVFTL ALWGFFPVV ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CVGGLLTMV FIAGNSAYE >polymerase“ MERIKELRDLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPALRMKWMMAMK YPITAD KRIMEMIPERNEQGQTLWSKTNDAGSDRVMVSPLAVTWWNRNGPTTSTVHYPK VYKTYFE KVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGA RILTSE SQLTITKEKKEELQDCKIAPLMVAYMLERELVRKTRFLPVAGGTSSVYIEVLHLTQ GTCW EQMYTPGGEVRNDDVDQSLIIAARNIVRRATVSADPLASLLEMCHSTQIGGIRMV DILRQ NPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTNGSSVKKEEEVLTGNLQTLKIKV HEGY EEFTMVGRRATAILRKATRRLIQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGD LNF...

11. Prediction algorithms MHC binding data Prediction algorithms Genome scans

12. Antigen Discovery Lauemøller et al., 2000

13. 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

14. Experimental validation of Bioinformatics predictions Wang et al., 2006

15. Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3:e91. Results are deposited in public databases

16. Figure by Anne Mølgaard, peptide (KVDDTFYYV) used as vaccine by Snyder et al. J Virol 78, 7052-60 (2004). Human MHC: ~1000 variants distributed over 12 types Virus epitope: up to 20 9 variants

17. Coverage of HLA alleles Clustering in: O Lund et al., Immunogenetics. 2004 55:797-810 SupertypeSelected allele A1 A*0101 A2 A*0201 A3 A*1101 A24A*2401 A26 (new*)A*2601 B7 B*0702 B8 (new*)B*0801 B27 B*2705 B39(new*)B*3901 B44 B*4001 B58 B*5801 B62B*1501

18. Polyvalent vaccines The equivalent of this in epitope based vaccines is to select epitopes in a way that that they together cover all strains. Strain 1 Strain 2 Strain 1 Strain 2 Epitope Uneven coverage, Average coverage = 2 Even coverage, Average coverage = 2

19. Processing

20. Proteasome specificity Low polymorphism –Constitutive & Immuno- proteasome Evolutionary conserved Stochastic and low specificity –Only 70-80% of the cleavage sites are reproduced in repeated experiments

21. Proteasome evolution (  1 unit) Constitutive Immuno Human (Hs) - Human Drosophila (Dm) - Fly Bos Taurus (Bota) - Cow Oncorhynchus mykiss (Om) - Fish …

22. Immuno- and Constitutive proteasome specificity... LVGPTPVNIIGRNMLTQL.. P1P1’ Immuno Constitutive

23. NetChop –Neural network based method PaProc –Weight matrix based method FragPredict –Based on a statistical analysis of cleavage- determining amino acid motifs present around the scissile bond i.e. also weight matrix like Predicting proteasomal cleavage

24. NetChop 3.0 Cterm (MHC ligands) LDFVRFMGVMSSCNNPA LVQEKYLEYRQVPDSDP RTQDENPVVHFFKNIVT TPLIPLTIFVGENTGVP LVPVEPDKVEEATEGEN YMLDLQPETTDLYCYEQ PVESMETTMRSPVFTDN ISEYRHYCYSLYGTTLE AAVDAGMAMAGQSPVLR QPKKVKRRLFETRELTD LGEFYNQMMVKAGLNDD GYGGRASDYKSAHKGLK KTKDIVNGLRSVQTFAD LVGFLLLKYRAREPVTK SVDPKNYPKKKMEKRFV SSSSTPLLYPSLALPAP FLYGALLLAEGFYTTGA NetChop-3.0 C-term –Trained on class I epitopes –Most epitopes are generated by the immuno proteasome –Predicts the immuno proteasome specificity

25. NetChop20S-3.0 In vitro digest data from the constitutive proteasome Toes et al., J.exp.med. 2001

26. Prediction performance TP FP AP AN A roc =0.5 A roc =0.8 1 - spec Sens

27. Predicting proteasomal cleavage NetChop20S-3.0 NetChop-3.0 Relative poor predictive performance –For MHC prediction CC~0.92 and AUC~0.95

28. Proteasome specificity NetChop is one of the best available cleavage method –www.cbs.dtu.dk/services/NetChop-3.0

29. Transporter associated with antigen processing (TAP)

30. What does TAP do?

31. TAP affinity prediction Transporter Associated with antigen Processing Binds peptides 9-18 long Binding determined mostly by N1-3 and C terminal amino acids

32. Integrating all three steps (protesaomal cleavage, TAP transport and MHC binding) should lead to improved identification of peptides capable of eliciting CTL responses Integration?

33. Identifying CTL epitopes 1 EBN3_EBV YQAYSSWMY 2.56 1.00 0.03 0.34 0.99 0.02 0.01 0.75 0.94 0.92 2.97 0 2.80 2 EBN3_EBV QSDETATSH 2.22 0.01 0.28 0.88 0.04 0.83 0.51 0.30 0.11 0.99 -0.80 0 2.28 3 EBN3_EBV PVSPAVNQY 1.55 0.01 0.97 0.01 0.22 0.21 1.00 0.02 0.04 1.00 2.63 0 1.78 4 EBN3_EBV AYSSWMYSY 1.31 0.34 0.99 0.02 0.01 0.75 0.94 0.92 0.09 1.00 3.28 1 1.58 5 EBN3_EBV LAAGWPMGY 1.02 1.00 0.97 0.22 0.01 0.18 0.01 0.06 0.01 1.00 3.01 0 1.27 6 EBN3_EBV IVQSCNPRY 0.99 0.10 0.97 0.50 0.05 0.01 0.01 0.01 0.02 0.93 3.19 0 1.24 7 EBN3_EBV FLQRTDLSY 0.94 0.46 0.99 0.02 0.82 0.07 0.01 0.63 0.01 0.96 2.79 0 1.18 8 EBN3_EBV YTDHQTTPT 1.15 1.00 0.01 0.42 0.02 0.04 0.01 0.02 0.54 0.14 -0.87 0 1.12 9 EBN3_EBV GTDVVQHQL 0.96 0.01 0.02 0.03 0.99 1.00 0.02 0.46 0.30 1.00 0.53 0 1.09... HLA affinityProteasomal cleavageTAP affinity

35. Large scale method validation HIV A3 epitope predictions

36. Class II MHC binding MHC class II binds peptides in the class II antigen presentation pathway Binds peptides of length 9-18 (even whole proteins can bind!) Binding cleft is open Binding core is 9 aa Peptide: up to 20 9 variants Human MHC II: ~1000 variants

37. Cartoon by Eric Reits Humoral immunity

38. Antibody - Antigen interaction Fab Antigen Epitope Paratope Antibody The antibody recognizes structural properties of the surface of the antigen

39. Discontinuous B-cell epitopes An example: An epitope of the Outer Surface Protein A from Borrelia Burgdorferi (1OSP) SLDEKNSVSVDLPGEM KVLVSKEKNKDGKYDLI ATVDKLELKGTSDKNN GSGVLEGVKADKCKVK LTISDDLGQTTLEVFKE DGKTLVSKKVTSKDKS STEEKFNEKGEVSEKIIT RADGTRLEYTGIKSDGS GKAKEVLKG 1OSP, Li et al. 1997

40. The DiscoTope web server www.cbs.dtu.dk/services/DiscoTope http://tools.immuneepitope.org/stools/discotope/discotope.do

41. Prediction servers at CBS for Immunological features BepiPred » Linear B-cell epitopesDiscoTope » Discontinuous B-cell epitopes NetChop » Proteasomal cleavages (MHC ligands)NetCTL » Integrated class I antigen presentation NetCTLpan Pan-specific integrated class I antigen presentation NetMHC » Binding of peptides to MHC class I allelesNetMHCII » Binding of peptides to MHC class II alleles NetMHCIIpan » Pan-specific binding of peptides to MHC class II HLA-DR alleles of known sequence NetMHCpan » Pan-specific binding of peptides to MHC class I alleles of known sequence VDJsolver » Analysis of human immunoglobulin VDJ recombination