1. Immunological Bioinformatics

2. The Immunological Bioinformatics group Immunological Bioinformatics group, CBS, Technical University of Denmark (www.cbs.dtu.dk)www.cbs.dtu.dk Ole Lund, Group Leader Morten Nielsen, Associate Professor Claus Lundegaard, Associate Professor Jean Vennestrøm, post doc. Thomas Blicher (50%), post doc. Mette Voldby Larsen, PhD student Pernille Haste Andersen, PhD student Sune Frankild, PhD student Sheila Tang, PhD student Thomas Rask (50%), PhD student Nicolas Rapin, PhD student Ilka Hoff, PhD student Jorid Sørli, PhD student Hao Zhang, PhD student MSc students 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 RossiSimulation of the Vladimir BrusicImmune system University of Utrectht Can KesmirIdeas

3. Figure 1-20

4. Effectiveness of vaccines 1958 start of small pox eradication program

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

6. The innate immune system Unspecific Antigen independent Immediate response No training/selection hence no memory Pathogen independent (but response might be pathogen type dependent)

7. The adaptive immune system Pathogen specific –Humoral –Cellular http://www.uni-heidelberg.de/zentral/ztl/grafiken_bilder/bilder/e-coli.jpg Bacteria http://en.wikipedia.org/wiki/Image:Aids_virus.jpg Virus http://tpeeaupotable.ifrance.com/ma%20photo/bilharzoze.jpg Parasite

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

9. Cartoon by Eric Reits Humoral immunity

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

11. Cellular Immunity

12. Anchor positions MHC class I with peptide

13. HLA specificity clustering A0201 A0101 A6802 B0702

14. Prediction of HLA binding specificity Historical overview Simple Motifs –Allowed/non allowed amino acids Extended motifs –Amino acid preferences (SYFPEITHI)SYFPEITHI) –Anchor/Preferred/other amino acids Hidden Markov models –Peptide statistics from sequence alignment SVMs and neural networks –Can take sequence correlations into account

15. SLLPAIVEL YLLPAIVHI TLWVDPYEV GLVPFLVSV KLLEPVLLL LLDVPTAAV LLDVPTAAV LLDVPTAAV LLDVPTAAV VLFRGGPRG MVDGTLLLL YMNGTMSQV MLLSVPLLL SLLGLLVEV ALLPPINIL TLIKIQHTL HLIDYLVTS ILAPPVVKL ALFPQLVIL GILGFVFTL STNRQSGRQ GLDVLTAKV RILGAVAKV QVCERIPTI ILFGHENRV ILMEHIHKL ILDQKINEV SLAGGIIGV LLIENVASL FLLWATAEA SLPDFGISY KKREEAPSL LERPGGNEI ALSNLEVKL ALNELLQHV DLERKVESL FLGENISNF ALSDHHIYL GLSEFTEYL STAPPAHGV PLDGEYFTL GVLVGVALI RTLDKVLEV HLSTAFARV RLDSYVRSL YMNGTMSQV GILGFVFTL ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CLGGLLTMV FIAGNSAYE KLGEFYNQM KLVALGINA DLMGYIPLV RLVTLKDIV MLLAVLYCL AAGIGILTV YLEPGPVTA LLDGTATLR ITDQVPFSV KTWGQYWQV TITDQVPFS AFHHVAREL YLNKIQNSL MMRKLAILS AIMDKNIIL IMDKNIILK SMVGNWAKV SLLAPGAKQ KIFGSLAFL ELVSEFSRM KLTPLCVTL VLYRYGSFS YIGEVLVSV CINGVCWTV VMNILLQYV ILTVILGVL KVLEYVIKV FLWGPRALV GLSRYVARL FLLTRILTI HLGNVKYLV GIAGGLALL GLQDCTMLV TGAPVTYST VIYQYMDDL VLPDVFIRC VLPDVFIRC AVGIGIAVV LVVLGLLAV ALGLGLLPV GIGIGVLAA GAGIGVAVL IAGIGILAI LIVIGILIL LAGIGLIAA VDGIGILTI GAGIGVLTA AAGIGIIQI QAGIGILLA KARDPHSGH KACDPHSGH ACDPHSGHF SLYNTVATL RGPGRAFVT NLVPMVATV GLHCYEQLV PLKQHFQIV AVFDRKSDA LLDFVRFMG VLVKSPNHV GLAPPQHLI LLGRNSFEV PLTFGWCYK VLEWRFDSR TLNAWVKVV GLCTLVAML FIDSYICQV IISAVVGIL VMAGVGSPY LLWTLVVLL SVRDRLARL LLMDCSGSI CLTSTVQLV VLHDDLLEA LMWITQCFL SLLMWITQC QLSLLMWIT LLGATCMFV RLTRFLSRV YMDGTMSQV FLTPKKLQC ISNDVCAQV VKTDGNPPE SVYDFFVWL FLYGALLLA VLFSSDFRI LMWAKIGPV SLLLELEEV SLSRFSWGA YTAFTIPSI RLMKQDFSV RLPRIFCSC FLWGPRAYA RLLQETELV SLFEGIDFY SLDQSVVEL RLNMFTPYI NMFTPYIGV LMIIPLINV TLFIGSHVV SLVIVTTFV VLQWASLAV ILAKFLHWL STAPPHVNV LLLLTVLTV VVLGVVFGI ILHNGAYSL MIMVKCWMI MLGTHTMEV MLGTHTMEV SLADTNSLA LLWAARPRL GVALQTMKQ GLYDGMEHL KMVELVHFL YLQLVFGIE MLMAQEALA LMAQEALAF VYDGREHTV YLSGANLNL RMFPNAPYL EAAGIGILT TLDSQVMSL STPPPGTRV KVAELVHFL IMIGVLVGV ALCRWGLLL LLFAGVQCQ VLLCESTAV YLSTAFARV YLLEMLWRL SLDDYNHLV RTLDKVLEV GLPVEYLQV KLIANNTRV FIYAGSLSA KLVANNTRL FLDEFMEGV ALQPGTALL VLDGLDVLL SLYSFPEPE ALYVDSLFF SLLQHLIGL ELTLGEFLK MINAYLDKL AAGIGILTV FLPSDFFPS SVRDRLARL SLREWLLRI LLSAWILTA AAGIGILTV AVPDEIPPL FAYDGKDYI AAGIGILTV FLPSDFFPS AAGIGILTV FLPSDFFPS AAGIGILTV FLWGPRALV ETVSEQSNV ITLWQRPLV Sequence information

16. Score sequences to weight matrix by looking up and adding L values from the matrix A R N D C Q E G H I L K M F P S T W Y V 1 0.6 0.4 -3.5 -2.4 -0.4 -1.9 -2.7 0.3 -1.1 1.0 0.3 0.0 1.4 1.2 -2.7 1.4 -1.2 -2.0 1.1 0.7 2 -1.6 -6.6 -6.5 -5.4 -2.5 -4.0 -4.7 -3.7 -6.3 1.0 5.1 -3.7 3.1 -4.2 -4.3 -4.2 -0.2 -5.9 -3.8 0.4 3 0.2 -1.3 0.1 1.5 0.0 -1.8 -3.3 0.4 0.5 -1.0 0.3 -2.5 1.2 1.0 -0.1 -0.3 -0.5 3.4 1.6 0.0 4 -0.1 -0.1 -2.0 2.0 -1.6 0.5 0.8 2.0 -3.3 0.1 -1.7 -1.0 -2.2 -1.6 1.7 -0.6 -0.2 1.3 -6.8 -0.7 5 -1.6 -0.1 0.1 -2.2 -1.2 0.4 -0.5 1.9 1.2 -2.2 -0.5 -1.3 -2.2 1.7 1.2 -2.5 -0.1 1.7 1.5 1.0 6 -0.7 -1.4 -1.0 -2.3 1.1 -1.3 -1.4 -0.2 -1.0 1.8 0.8 -1.9 0.2 1.0 -0.4 -0.6 0.4 -0.5 -0.0 2.1 7 1.1 -3.8 -0.2 -1.3 1.3 -0.3 -1.3 -1.4 2.1 0.6 0.7 -5.0 1.1 0.9 1.3 -0.5 -0.9 2.9 -0.4 0.5 8 -2.2 1.0 -0.8 -2.9 -1.4 0.4 0.1 -0.4 0.2 -0.0 1.1 -0.5 -0.5 0.7 -0.3 0.8 0.8 -0.7 1.3 -1.1 9 -0.2 -3.5 -6.1 -4.5 0.7 -0.8 -2.5 -4.0 -2.6 0.9 2.8 -3.0 -1.8 -1.4 -6.2 -1.9 -1.6 -4.9 -1.6 4.5 Scoring a sequence to a weight matrix RLLDDTPEV GLLGNVSTV ALAKAAAAL Which peptide is most likely to bind? Which peptide second? 11.9 14.7 4.3 84nM 23nM 309nM

17. Example from real life 10 peptides from MHCpep database Bind to the MHC complex Relevant for immune system recognition Estimate sequence motif and weight matrix Evaluate motif “correctness” on 528 peptides lALAKAAAAM lALAKAAAAN lALAKAAAAR lALAKAAAAT lALAKAAAAV lGMNERPILT lGILGFVFTM lTLNAWVKVV lKLNEPVLLL lAVVPFIVSV

18. Prediction accuracy Pearson correlation 0.45 Prediction score Measured affinity

19. Predictive performance

20. Higher order sequence correlations Neural networks can learn higher order correlations! –What does this mean? S S => 0 L S => 1 S L => 1 L L => 0 No linear function can learn this (XOR) pattern Say that the peptide needs one and only one large amino acid in the positions P3 and P4 to fill the binding cleft How would you formulate this to test if a peptide can bind?

21. 313 binding peptides313 random peptides Mutual information

22. Sequence encoding (continued) Sparse encoding V:0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 L:0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 V. L=0 (unrelated) Blosum encoding V: 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 L:-1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 R:-1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 V. L = 0.88 (highly related) V. R = -0.08 (close to unrelated)

23. Evaluation of prediction accuracy

24. Network ensembles No one single network with a particular architecture and sequence encoding scheme, will constantly perform the best Also for Neural network predictions will enlightened despotism fail –For some peptides, BLOSUM encoding with a four neuron hidden layer can best predict the peptide/MHC binding, for other peptides a sparse encoded network with zero hidden neurons performs the best –Wisdom of the Crowd Never use just one neural network Use Network ensembles

25. Evaluation of prediction accuracy ENS : Ensemble of neural networks trained using sparse, Blosum, and hidden Markov model sequence encoding

26. NetMHC-3.0 update IEDB + more proprietary data Higher accuracy for existing ANNs More Human alleles Non human alleles (Mice + Primates) Prediction of 8mer binding peptides for some alleles Prediction of 10- and 11mer peptides for all alleles Outputs to spread sheet

27. Prediction of 10- and 11mers using 9mer prediction tools Approach: For each peptide of length L create 6 pseudo peptides deleting a sliding window of L- 9 always keeping pos. 1,2,3, and 9 Example: MLPQWESNTL =MLPWESNTL MLPQESNTL MLPQWSNTL MLPQWENTL MLPQWESTL MLPQWESNL

28. Prediction of 10- and 11mers using 9mer prediction tools

29. Final prediction = average of the 6 log scores: (0.477+0.405+0.564+0.505+0.559+0.521)/6 = 0.505 Affinity: Exp(log(50000)*(1 - 0.505))= 211.5 nM

30. Prediction using ANN trained on 10mer peptides

31. Prediction of 10- and 11mers using 9mer prediction tools

32. Cellular Immunity

33. 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

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

35. Predicting TAP affinity 9 meric peptides>9 meric Peters et el., 2003. JI, 171: 1741. ILRGTSFVYV -0.11 + 0.09 - 0.42 - 0.3 = -0.74

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

37. 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

40. Large scale method validation HIV A3 epitope predictions

41. Case I: SARS Sylvester-Hvid et al, Tissue Antigens. 2004

42. Sars virus HLA ligands 75% of predicted peptides were binding with an IC 50 <500 nM

43. Case II: Discovery of conserved Class I epitopes in Human Influenza Virus H1N1 Wang et al., Vaccine 2007

44. Pox Strategy

45. Influenza We selected the Influenza peptides with the top 15 combined scores with conservation p9 > 70% for each pf the 12 supertypes. 180 peptides selected 167 tested for binding and CTL response 89 (53%) of the influenza peptides tested have an affinity better than 500nM

46. Donors 35 normal healthy blood donors 35-65 years old Expected to have had influenza more than 3 times HLA typed by SBT for HLA A and B

47. 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 have earlier reacted to the peptide (during a response of a vaccine/natural infection) FLDVMESM Two spots FLDVMESM

48. Peptides positive in ELISPOT assay Protein Supertype Affinity PASACombCons_9 Polymerase PB1A1 nt0.623.253.740.90 Nucleoprotein NPA1 nt0.552.893.350.80 Polymerase PB1A2 510.461.091.250.76 Polymerase PB1A26 511.31.592.050.98 Polymerase PB1B272460.431.542.020.97 Nucleoprotein NPB27 370.371.341.720.87 Matrix protein M1B39 nt7.080.991.290.84 Nucleoprotein NPB58 410.441.511.640.99 Polymerase PB1B621780.400.961.470.97 Polymerase PB1B62 870.461.081.450.92 Polymerase PB1B7 50.671.872.080.99 Polymerase PAB8 nb7.811.051.280.77 Protein: Common name for protein Supertype: HLA supertype that the peptide is predicted to bind to Affinity:Measured affinity (kD in nM) PA:Predicted affinity SA:Scaled affinity Comb:Combined score calculated as in Larsen et al., 2005 cons_9:Fraction of clusters (with more than 98% sequence identity) that contain the 9mer nb: non binder nt: not tested

49. Peptides positive in ELISPOT assay

50. Conservation of epitopes Number of 9mers 100% conserved: 10/12 conserved in Influenza A virus (A/Goose/Guangdong/1/96(H5N1)) 11/12 conserved in Influenza A virus (A/chicken/Jilin/9/2004(H5N1))

51. EpiSelect Genotype 1 Top Scoring Peptides Genotype 2 Genotype 3 Genotype 4 Genotype 5 Genotype 6 Select peptide with maximal coverage Select peptide with maximal coverage preferring uncovered strains Select peptide with maximal coverage preferring lowest covered strains Repeat until the desired number of peptides is selected

52. HCV Results - B7 Genotype 1 Genotype 2 Genotype 3 Genotype 4 Genotype 5 Genotype 6 QPRGRRQPI Peptide Predicted affinity (nM) 5 SPRGSRPSW43 GenomeCoverage 5 4 DPRRRSRNL * 366 RARAVRAKL Peptides 63 TPAETTVRL * 383 3 3 2 3 4 3 * Verified B7 supertype restricted CD8+ epitope in the Los Alamos HCV epitope database

53. Ongoing work Selection of epitopes covering host (HLA) and pathogen variability Selection of diagnostic peptides in TB Predict cross reactivity (T and B cell) –Applications in epitope prediction, autoimmune diseases, transplantation Virulence factor discovery by comparative genomics Function-antigenecity studies Bioinformatics immune system simulation