1. Immunological Bioinformatics: Prediction of epitopes in pathogens Ole Lund

2. 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...

3. Prediction algorithms MHC binding data Prediction algorithms Genome scans

4. 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

5. Arms race between humans and microbes Recognize Escape Peptides from microbes HLA molecules In Humans

6. 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 Peptide: up to 20 9 variants

7. HLA A and B diversity Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Roder G, Peters B, Sette A, Lund O, Buus S., NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE. 2007 2:e796.

8. Binding affinity vs antigenecity A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. Assarsson E, Sidney J, Oseroff C, Pasquetto V, Bui HH, Frahm N, Brander C, Peters B, Grey H, Sette A. J Immunol. 2007 Jun 15;178(12):7890-901.

9. Prediction of MHC I epitopes Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Lundegaard C, Lund O, Buus S, Nielsen M. Immunology. 2010 Jul;130(3):309-18. Epub 2010 May 26. Review.

10. Recent benchmark studies Class I –Peters B, Bui HH, Frankild S et al. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLoS Comput Biol 2006; 2:e65. –Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol 2008; 9:8. Class II –Wang P, Sidney J, Dow C, Mothe B, Sette A, Peters B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach.PLoS Comput Biol 2008; 4:e1000048. –Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II pep- tide binding prediction servers: applications for vaccine research. BMC Bioinformatics 2008; 9(Suppl. 12):S22. –Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and toolsLianming Zhang, Keiko Udaka, Hiroshi Mamitsuka, Shanfeng ZhuBriefings in bioinformatics (impact factor: 7.33). 09/2011; DOI: 10.1093/bib/bbr060

11. Validation of binding predictions

12. Response diversity Hoof, et al., JI, 2010

13. TB epitope discovery strategy Tang ST et al Submitted. Mtb H37Rv genome sequence Selection of peptides predicted to bind to HLA supertypes (NetCTL, protFun, SubCell): A2 (A*0201), A3 (A*0301), B7 (B*0702) (coverage approx. 80% of the world population) Synthesis selected peptides Measuring peptide/MHC binding affinity in vitro Screening for peptide recognition in in vitro CD8 + T cell assay in healthy PPD + donors Direct ex vivo determination of frequencies of peptide/tetramer + CD8 + T cells in TB patients (Multi) functionality of peptide responsive CD8 + T cells in TB patients Genome-Based In Silico Identification of New Mycobacterium tuberculosis Antigens Activating Polyfunctional CD8+ T Cells in Human Tuberculosis. Tang ST, van Meijgaarden KE, Caccamo N, Guggino G, Klein MR, van Weeren P, Kazi F, Stryhn A, Zaigler A, Sahin U, Buus S, Dieli F, Lund O, Ottenhoff TH. J Immunol. 2011 Jan 15;186(2):1068-80. Epub 2010 Dec 17.

14. TB Genome-Based In Silico Identification of New Mycobacterium tuberculosis Antigens Activating Polyfunctional CD8+ T Cells in Human Tuberculosis. Tang ST, van Meijgaarden KE, Caccamo N, Guggino G, Klein MR, van Weeren P, Kazi F, Stryhn A, Zaigler A, Sahin U, Buus S, Dieli F, Lund O, Ottenhoff TH. J Immunol. 2011 Jan 15;186(2):1068-80. Epub 2010 Dec 17.

15. TB Genome-Based In Silico Identification of New Mycobacterium tuberculosis Antigens Activating Polyfunctional CD8+ T Cells in Human Tuberculosis. Tang ST, van Meijgaarden KE, Caccamo N, Guggino G, Klein MR, van Weeren P, Kazi F, Stryhn A, Zaigler A, Sahin U, Buus S, Dieli F, Lund O, Ottenhoff TH. J Immunol. 2011 Jan 15;186(2):1068-80. Epub 2010 Dec 17.

16. Tetramer and cytokine staining of 10 cured TB patients and 10 healthy controls Genome-Based In Silico Identification of New Mycobacterium tuberculosis Antigens Activating Polyfunctional CD8+ T Cells in Human Tuberculosis. Tang ST, van Meijgaarden KE, Caccamo N, Guggino G, Klein MR, van Weeren P, Kazi F, Stryhn A, Zaigler A, Sahin U, Buus S, Dieli F, Lund O, Ottenhoff TH. J Immunol. 2011 Jan 15;186(2):1068-80. Epub 2010 Dec 17.

17. The challenge of rational epitope selection We have more than 2500 MHC molecules We often have more than 500 different pathogenic strains How to design a method to select a small pool of peptides that will cover both the MHC polymorphism and the pathogen diversity? –No peptide will bind to all MHC molecules and few (maybe even no) peptides will be present in all pathogenic strains

18. Vaccine discovery - HIV case story 10 HIV proteins –> 2,000,000 different peptides exist within the known HIV clades Patient diversity –More than 2500 different MHC molecules The challenge –Select 100 (0.005%) peptides with optimal genomic and HLA coverage

19. HIV Gag phylogenetic tree Clade C Clade D Clade B Clade A Clade AE Few peptides conserved between all viral strains

20. Dodo: Flavi viruses

21. Predicted West Nile virus Epitopes Mette Voldby Larsen

22. Sequence identity vs. serotype Solmaz Gabery

23. Epitope identification 56 (1.5%) 9mer are conserved among all 15 Clade A gag sequences

24. Polyvalent vaccines Select epitopes in a way so 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 X

25. EpiSelect. Pathogen diversity

26. Selected West Nile Virus Epitopes Shown relative to NC001563/M12296 Mette Voldby Larsen

27. Use of EpiSelect: CTL Epitopes with Maximum HIV-1 Coverage Problem: The high mutation rate of HIV-1 makes it difficult to identify CTL epitopes that are conserved among all subtypes. Possible solution: Chose a number of predicted and experimentally identified epitopes that together constitute a broad coverage of the HIV-1 strains examined. Data: 300+ fully sequenced HIV-1 strains A (A1 and A2), B, C, D, and CRF01_AE Methods: Prediction of CTL epitopes restricted by A1, A2, A3, A24, B7, B44, or B58 Select the epitopes that give the broadest coverage The algorithm chooses epitopes found in as many strains as possible, while up prioritizing epitopes from strains with few already-selected epitopes. Results: The final set consists of 180 epitopes. On average, each strain is covered by 54 epitopes (minimum 29). Ongoing work by Annika Karlsson: The ability of the chosen epitopes to elicit CTL response will be examined by using PBMCs from HIV-1 infected patients. Annika Karlsson and Carina Perez

28. SupertypesPhenotype frequencies CaucasianBlackJapaneseChineseHispanicAverage A2,A3, B783 %86 %88 %88 %86 %86% +A1, A24, B44100 %98 %100 %100 %99 %99 % +B27, B58, B62100 %100 %100 %100 %100 %100 % HLA polymorphism - frequencies Sette et al, Immunogenetics (1999) 50:201-212

29. Perez et al., JI, 2008 Annika Karlsson Karolinska Institute Carina Perez Response of 31 HIV infected patients to 184 predicted HIV epitopes

30. All HIV responsive patients respond to at least one of nine peptides Perez et al., JI, 2008

31. PopCover – 2D searching > 2,000,000 different peptides exists within the known HIV clades 227091 peptides with prediction binding affinity stronger than 500 nM to any MHC molecule – 5608(tat), 20961(nef), 31848(gag),42748(pol),125926 (env) No Gag peptides are found in all clades and 92% of all Gag peptides are shared only between 0-5% of all clades The challenge Select 64 (less than 0.001%) peptides with optimal genomic and HLA coverage –tat(4), nef(15), gag(15), pol(15), env(15)

32. EpiSelect and PoPCover EpiSelect The sum is over all genomes i. P j i is 1 if epitope j is present in genome i. C i is the number of times genome i has been targeted in the already selected set of epitopes PopCover The sum is over all genomes i and HLA alleles k. R j ki is 1 if epitope j is present in genome i and is presented by allele k, and E ki is the number of times allele k has been targeted by epitopes in genome i by the already selected set of epitopes, f k is the frequency of allele k in a given population and g i is the genomes frequency

33. 20 juni 2015Marcus Buggert33 An average of 4,79 recognized peptides per patient Tat Nef Gag Pol Env Marcus Buggert et al., In preparation Experimental validation of HIV class II epitopes

34. Experimental validation

35. Vaccine design. Polytope construction NH2 COOH Epitope Linker M C-terminal cleavage Cleavage within epitopes New epitopes cleavage

36. Polytope starting configuration Immunological Bioinformatics, The MIT press.

37. Polytope optimal configuration Immunological Bioinformatics, The MIT press.

38. Prediction servers at CBS Web servers CTL epitopes www.cbs.dtu.dk/services/NetCTL MHC binding www.cbs.dtu.dk/services/NetMHC www.cbs.dtu.dk/services/NetMHCII www.cbs.dtu.dk/services/NetMHCpan www.cbs.dtu.dk/services/NetMHCcons www.cbs.dtu.dk/services/NetMHCIIpan www.cbs.dtu.dk/services/HLArestrictor MHC Motif viewer www.cbs.dtu.dk/biotools/MHCMotifViewer/Home.html Proteasome processing www.cbs.dtu.dk/services/NetChop-3.0 B-cell epitopes www.cbs.dtu.dk/services/BepiPred/ www.cbs.dtu.dk/services/DiscoTope Plotting of epitopes relative to reference sequence www.cbs.dtu.dk/services/EpiPlot-1.0 Analysis of human immunoglobulin VDJ recombination www.cbs.dtu.dk/services/VDJsolver Geno-pheno type association based mapping of binding sites www.cbs.dtu.dk/services/SigniSite PhD/master course in Immunological Bioinformatics, June, 2012 www.cbs.dtu.dk/courses/27685.imm

39. Peters B, et al. Immunogenetics. 2005 57:326-36, PLoS Biol. 2005 3:e91. Immune Epitope Database (IEDB)

40. Cross-reactivity Crossreactivity is predictable (Pearsons r = 0.35-0.6) –Rule of thumb: Each mutation halfs the response Frankild et al., PLoS ONE 3(3): e1831 Hoof, et al., JI, 2010

41. Pilot study of immunogenecity based on DrugBank www.drugbank.ca Records corresponding to 123 FDA-approved biotech (protein/peptide) drugs were downloaded Sequences were compared to the human proteome (sequences from “Homo Sapiens” in NR (non redundant database from NCBI)) using blast. Sequences found in DrugBank and NR need to be manually validated/curated

42. Types of proteins Human/Human protein sequence Identical proteins Modified/allelic human proteins Non human proteins Antibodies Non human Human-murine chimaer Humanized Human Who – Allelic differences of VDJ genes How much – Break tolerance Tolerance to own B cell receptors?

43. Proposed application in assessment of protein drugs 1Compare amino acid sequence of drug with the human proteome 2Predict epitopes in regions that differ from the human proteome 3Select representative HLA alleles 4Verify binding experimentally 5Assess predicted immunogenecity using blood from treated patients/transgenic animals/naïve donors 6Compare with clinical findings of immunogenecity/adverse effects/lack of effect

44. Data acquired Data on 33 approved therapeutic proteins Julie Serritslev, Jens Vindahl Kringelum, et al., in preparation Immunogenicity Percent of recipients in a clinical study that had detectable antibodies against the therapeutic protein The primary source of immune response data was the reviewed data presented in Meyler's Side Effects of Drugs and from FDA labels.

45. Alleles representative of HLA-A, HLA-B and HLA-DRB, HLA-DQB1 and four HLA-DPB1 super-types Julie Serritslev, Jens Vindahl Kringelum, et al., in preparation; Nielsen et al., 2008

46. Prediction of epitopes MHC Class I and II binders can be predicted for all known alleles (A ROC ~ 0.8- 0.9) Binding correlates with likelihood of response No epitope give response in all individuals Cross reactivity correlates with epitope similarity B cell epitopes are hard to predict (A ROC ~ 0.6-0.7)