In silico methods for rational design of vaccine and immunotherapeutics

Transcription

1 In silico methods for rational design of vaccine and immunotherapeutics Morten Nielsen, CBS, Department of Systems Biology, DTU and Instituto de Investigaciones Biotecnológicas, Universidad de San Martín, Argentina

2 Bioinformatics in a nutshell List of peptides that have a given biological feature YMNGTMSQV GILGFVFTL ALWGFFPVV ILKEPVHGV ILGFVFTLT LLFGYPVYV GLSPTVWLS WLSLLVPFV FLPSDFFPS CVGGLLTMV FIAGNSAYE Mathematical model (neural network, hidden Markov model) Search databases for other biological sequences with the same feature/property >polymerase! MERIKELRDLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPALRMKWMMAMKYPITAD! KRIMEMIPERNEQGQTLWSKTNDAGSDRVMVSPLAVTWWNRNGPTTSTVHYPKVYKTYFE! KVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSE! SQLTITKEKKEELQDCKIAPLMVAYMLERELVRKTRFLPVAGGTSSVYIEVLHLTQGTCW! EQMYTPGGEVRNDDVDQSLIIAARNIVRRATVSADPLASLLEMCHSTQIGGIRMVDILRQ! NPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTNGSSVKKEEEVLTGNLQTLKIKVHEGY! EEFTMVGRRATAILRKATRRLIQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNF!...!

3 Influenza A virus (A/Goose/Guangdong/ /96(H5N)) >Segment! agcaaaagcaggtcaattatattcaatatggaaagaataaaagaactaagagatctaatg! tcgcagtcccgcactcgcgagatactaacaaaaaccactgtggatcatatggccataatc! aagaaatacacatcaggaagacaagagaagaaccctgctctcagaatgaaatggatgatg! gcaatgaaatatccaatcacagcagacaagagaataatggagatgattcctgaaaggaat!! Genome and 3350 other nucleotides on 8 segments!! Proteins >polymerase! MERIKELRDLMSQSRTREILTKTTVDHMAIIKKYTSGRQEKNPALRMKWMMAMKYPITAD! KRIMEMIPERNEQGQTLWSKTNDAGSDRVMVSPLAVTWWNRNGPTTSTVHYPKVYKTYFE! KVERLKHGTFGPVHFRNQVKIRRRVDINPGHADLSAKEAQDVIMEVVFPNEVGARILTSE! SQLTITKEKKEELQDCKIAPLMVAYMLERELVRKTRFLPVAGGTSSVYIEVLHLTQGTCW! EQMYTPGGEVRNDDVDQSLIIAARNIVRRATVSADPLASLLEMCHSTQIGGIRMVDILRQ! NPTEEQAVDICKAAMGLRISSSFSFGGFTFKRTNGSSVKKEEEVLTGNLQTLKIKVHEGY! EEFTMVGRRATAILRKATRRLIQLIVSGRDEQSIAEAIIVAMVFSQEDCMIKAVRGDLNF!...! and 9 other proteins 9mer peptides MERIKELRD! ERIKELRDL! RIKELRDLM! IKELRDLMS! KELRDLMSQ! ELRDLMSQS! LRDLMSQSR! RDLMSQSRT! DLMSQSRTR! LMSQSRTRE! and 4376 other 9mers

4 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. Zhang GL, Ansari HR, Bradley P, et al.. Machine learning competition in immunology - Prediction of HLA class I binding peptides. J Immunol Methods. 20 Nov 30;374(-2):-4. Epub 20 Sep 29. 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:e Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinformatics 2008; 9(Suppl. 2):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. 09/20; DOI: 0.093/bib/bbr060

5 Prediction of epitopes. Summary Cytotoxic T cell epitope: (A ROC ~ 0.95) Will a given peptide bind to a given MHC class I molecule Helper T cell Epitope (A ROC ~ 0.85) Will a part of a peptide bind to a given MHC II molecule B cell epitope (A ROC ~ 0.80) Will a given part of a protein bind to one of the billions of different B Cell receptors

6 Validation of binding predictions

7 Epitope identification rates Pathogen Predicted Binding % Binding Epitopes % Epitopes Influenza virus A Vaccinia virus Mycobacterium tuberculosis West Nile virus Human immunodeciciency virus 84 ND ND % of predictions turn out to be binders Only 5-60% are epitopes

8 Limits in the success rate? Response diversity Hoof, et al., JI, 200

9 What defines a T cell epitope? MHC binding Processing (Proteasomal cleavage,tap) Other proteases T cell repertoire (similarity to self) and cross-reactivity MHC:peptide complex stability Source protein abundance, cellular location and function

10 Is there anything beyond MHC binding?

11 NO

12 MHC Class I pathway What about the other players? Figure by Eric A.J. Reits

13 MHC ligands prediction NetCTL (Larsen et al, 2005) Immuno proteasome TAP MHC

14 Evaluation. MHC ligands from SYFPEITHI Sort on binding Top Rank: AUC=.0 Random Rank: AUC=0.5

15 Processing Does proteasomal cleavage and TAP matter? NetCTL and MHC-pathway said yes (in 2005)

16 NetCTL, 2005 Wcl=0.05, Wt=0. (AUC)

17 20 says - processing has little impact Wcl=0, Wt=0 (AUC) - NetMHCpan Wcl=0.225, Wt= (AUC0.) - NetCTLpan Stranzle. Immunogenetics. 200 Jun;62(6):

18 Why is this? MHC class I pathway co-evolution Nielsen, Kesmir, Immunogenetics, (2005) 57: 33 4

19 What defines a T cell epitope? MHC binding Processing (Proteasomal cleavage,tap) ( ) Other proteases T cell repertoire (similarity to self) and cross-reactivity MHC:peptide complex stability Source protein abundance, cellular location and function

20 Other proteases Trimming prior to binding QRSPMFEGTL Rank=.8% RSPMFEGTL Rank =0% % Cytotoxicity QRSPMFEGTL RSPMFEGTL RSPMFEGTLG RSPMFEGT SPMFEGTL peptide conc. ng/ml BoLA Class I epitopes, Work by Ivan Morrison and co-workers

21 What defines a T cell epitope? MHC binding Processing (Proteasomal cleavage,tap) Other proteases T cell repertoire (similarity to self) and cross-reactivity MHC:peptide complex stability Source protein abundance, cellular location and function

22 T cell cross-reactivity Cross-reactivity is predictable (Pearsons r = ) T cell cross-reactivity to self Amino acid similarity accounts for T cell cross-reactivity and for "holes" in the T cell repertoire. Frankild et al., PLoS One, 2008

23 What defines a T cell epitope? MHC binding Processing (Proteasomal cleavage,tap) Other proteases T cell repertoire (similarity to self) and cross-reactivity MHC:peptide complex stability Source protein abundance, cellular location and function

24 MHC Class I Assay Technology Affinity Stability MHC D A β 2 m Peptid e SA High affinity Low affinity 0000 T ½ = 25 hours [pmhc], nm K D = 0 nm K D = 2000 nm CPM 000 T ½ = hours Stable Unstable [Peptide], nm Δt, 37 C Harndahl M, Rasmussen M, Roder G, Buus S.J Immunol Methods. 200 Oct 3.

25 MHC:peptide Stability Two peptides, at-cell epitope (FLTSVINRV, filled circles ) and a non-immunogenic peptide (NQNDNEETV, open circles) were compared with respect to affinity and stability. A) Peptide titration of two peptides binding with equal affinity to HLA- A*02:0, KD = 7nM. B) The stability of the same two peptides measured at 37 C. M.N Harndahl et al., Eur J Immunol. 202 Jun;42(6):405-6

26 Assarsson et al, JI, mer binders classified into 2 immunogenic, 6 subdominant, 29 cryptic and 26 non-immunogenic

27 MHC:peptide Stability Stability of 2 pairs (paired on affinity) of immunogenic and nonimmunogenic peptides. M.N Harndahl et al., Eur J Immunol. 202 Jun;42(6):405-6

28 Predicting stability. A02:0 peptides PCC=0.72

29 Predicting MHC:peptide Stability (292 HLA-A02:0 ligands) Sort on binding } ligand/nonligand pair 292 Affinity matched A020 SYFPHITHI ligands

30 Predicting MHC:peptide Stability (290 HLA-A02:0 ligands) P< Affinity matched A020 SYFPHITHI ligands

31 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

32 Vaccine discovery - HIV case story 0 HIV proteins > 2,000,000 different peptides exist within the known HIV clades Patient diversity More than 2500 different MHC molecules The challenge Select 00 (0.005%) peptides with optimal genomic and HLA coverage

33 HIV Gag phylogeny Clade C Few peptides conserved between all viral strains Clade AE Clade D Clade A Clade B

34 Epitope identification 56 (.5%) 9mer are conserved among all Clade A gag sequences

35 Polyvalent vaccines Select epitopes in a way so that they together cover all strains. Uneven coverage, Average coverage = 2 Strain Strain 2 X Epitope Even coverage, Average coverage = 2 Strain Strain 2

36 EpiSelect. Pathogen diversity S j G = P i j i δ +Ci Pérez CL. J Immunol. 2008

37 HLA polymorphism - frequencies Supertypes Phenotype frequencies Caucasian Black Japanese Chinese Hispanic Average A2,A3, B7 83 % 86 % 88 % 88 % 86 % 86% +A, A24, B44 00 % 98 % 00 % 00 % 99 % 99 % +B27, B58, B62 00 % 00 % 00 % 00 % 00 % 00 % Sette et al, Immunogenetics (999) 50:20-22

38 Response of 3 HIV infected patients to 84 predicted HIV epitopes Perez et al., JI, 2008

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

40 HLA supertypes

41 More on supertypes! A3 A24 A26 A A2

42 Problems with HLA supertypes Supertypes are not perfect, i.e. HLA-A*:0 and HLA-A*03:0 do not bind the same set of peptides Supertype representatives are not optimal representatives in all populations Guinea Bissau: A2402.5%, A230 6% A030 6%, A0 0% Hong Kong: A2402 5%, A230 0% A %, A0 29%

43 PopCover 2D searching > 2,000,000 different peptides exists within the known HIV clades peptides with prediction binding affinity stronger than 500 nm to any MHC molecule 5608(tat), 2096(nef), 3848(gag),42748(pol),25926 (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 0.00%) peptides with optimal genomic and HLA coverage tat(4), nef(5), gag(5), pol(5), env(5)

44 EpiSelect and PoPCover EpiSelect S j G = The sum is over all genomes i. P j i is 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 S j A+ G = P i j i δ +Ci i k j R ki f k g i β + E ik The sum is over all genomes i and HLA alleles k. R j ki is 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

45 Virtual validation Select 5 peptide from the a pool of 300 HCV genomes with optimal genome and HLAclass I coverage Make a set of 000 virtual patients infected with a random virus have HLA haplotype matching a given population Calculate successrate as fraction of patients presenting one or more of the 5 peptides

46 Experimental validation of HIV class II epitopes Tat Nef Gag Pol Env 38 HIV infected individuals An average of 4,79 recognized peptides per patient Buggert M, PLoS One. 202

47 Experimental validation Buggert M, PLoS One. 202

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

49 Polytope starting configuration Immunological Bioinformatics, The MIT press.

50 Polytope optimal configuration Immunological Bioinformatics, The MIT press.

51 Functional clustering of ligand binding domains

52 Using alignment Align A68:0 (365) versus A68:02 (365). Aln score Aln len 365 Id A68:0 0 MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFYTSVSRPGRGEPRFIAVGYVDDTQFVRFDSDAA ::::::::::::::::::::::::::::::::::: ::::::::::::::::::::::::::::: A68:02 0 MAVMAPRTLVLLLSGALALTQTWAGSHSMRYFYTSMSRPGRGEPRFIAVGYVDDTQFVRFDSDAA A68:0 65 SQRMEPRAPWIEQEGPEYWDRNTRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQMMYGCDVGSD ::::::::::::::::::::::::::::::::::::::::::::::::::::::: ::::::: : A68:02 65 SQRMEPRAPWIEQEGPEYWDRNTRNVKAQSQTDRVDLGTLRGYYNQSEAGSHTIQRMYGCDVGPD A68:0 30 GRFLRGYRQDAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQWRAYLEGTCVEWLRRY ::::::: : ::::::::::::::::::::::::::::::::::::::::::::::::::::::: A68:02 30 GRFLRGYHQYAYDGKDYIALKEDLRSWTAADMAAQTTKHKWEAAHVAEQWRAYLEGTCVEWLRRY A68:0 95 LENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPA ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: A68:02 95 LENGKETLQRTDAPKTHMTHHAVSDHEATLRCWALSFYPAEITLTWQRDGEDQTQDTELVETRPA A68:0 260 GDGTFQKWVAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGA ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: A68: GDGTFQKWVAVVVPSGQEQRYTCHVQHEGLPKPLTLRWEPSSQPTIPIVGIIAGLVLFGAVITGA A68:0 325 VVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV :::::::::::::::::::::::::::::::::::::::: A68: VVAAVMWRRKSSDRKGGSYSQAASSDSAQGSDVSLTACKV

53 Sequence based clustering B58_0 B5_0 B27_05 A24_02 A26_0 A02_0 A68_0 B40_0 A68_02 B39_0 B07_02 B08_0 A03_0 A0_0

54 Sequence logos HLA-A*6802 HLA-A*680 Seq2Logo:

55 NetMHCpan - a pan-specific method NetMHC NetMHCpan NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence. Nielsen et al. PLoS ONE 2007

56 HLA-A02:0 versus HLA-A68:02 A020 A6802 ISCDEGRFK TDRAAQTRE IAPLRMSAT KPAFKTGEE GVERHIHIF TYGWAWLLK AEDIAKTVA MSGNEIYDH EDVERGQVV ILVEHARVE QKPTLTVML AQKTIEWAQ VEHPNVYKM EERASSSKN EDRKGHDRR LQGTTDVTP NIGVILLLT MRLAHDPDA GEYLKEKIR IPRCSPPPP HLA-A PCC: HLA-A020

57 HLA-A02:0 versus HLA-A68:0 PCC: 0.09

58 Clusterring and binding motifs HLA-A68:02 d = PCC(A, B) HLA-A68:0 HLA-A68_02 HLA-A68_0 HLA-A02:0 HLA-A03:0 HLA-A02_0 HLA-A03_0 0.07

59 Specificity-based clustering (w logos) HLA-A68_02 HLA-B58_0 HLA-A24_02 HLA-A0_0 HLA-B5_0 HLA-A26_0 HLA-A02_0 HLA-A03_0 HLA-B27_05 HLA-B40_0 HLA-B39_0 HLA-B07_02 HLA-B08_0 HLA-A68_0

60 HLA-B40_02 HLA-A02_0 Patr-A0302 Patr-A040 Patr-B070 Patr-B350 HLA-A3_0 HLA-A34_0 Patr-A60 HLA-A23_0 Patr-B2202 Patr-B202 Patr-B30 Patr-A0902 HLA-B58_0 Patr-A060 HLA-B08_0 HLA-B27_05 HLA-A29_02 Patr-B200 HLA-B39_05 Patr-B290 HLA-A03_0 Patr-B080 Patr-B030 HLA-B40_0 Patr-A00 HLA-B44_02 Patr-B0 Patr-A080 Patr-A703 HLA-A68_0 HLA-B56_0 Patr-B020 HLA-B5_0 Patr-B0502 HLA-B49_0 Patr-A070 HLA-B46_0 Patr-B270 HLA-A_0 Patr-B2303 Patr-B40 Patr-A050 Patr-B300 Patr-A40 Patr-A80 HLA-B07_02 Patr-B040 HLA-A80_0 Patr-B70 HLA-A33_03 HLA-A30_02 Patr-A20 HLA-B5_0 Patr-B250 HLA-A02_04 Patr-B280 HLA-B48_0 HLA-B35_0 HLA-A26_0 Patr-B360 Patr-A00 Patr-B00 Patr-B00 Patr-B230 HLA-B57_0 Patr-A70 HLA-B45_0 Patr-B90 HLA-B39_06 Patr-A020 Patr-B60 Patr-B220 Patr_AL Patr-B370 HLA-A0_0 HLA-A02_06 Patr-A0 HLA-B3_0 Patr-A50 Patr-A090 Patr-B090 HLA-B39_0 Patr-A240 Patr-A230 HLA-A24_02 Patr-B80 Patr-A30 Patr-B260 Patr-B20 Patr-B240 Patr-B703 Patr-A030 Patr-B050 Patr-B060 HLA-A68_ A3 A24 B62 A B27 B39 B44 A2 B59 A26 B7 B8 The selective sweep theory and the lost chimpanzee alleles A26, B27, B62 and A2 are lost from the chip repertoire B58

61 SLA (Swine) and men HLA-A260 HLA-B2705 HLA-B400 HLA-B0702 HLA-B080 HLA-B390 HLA-A030 HLA-A020 HLA-A2402 SLA-200 SLA-3040 SLA-00 SLA-080 SLA-040 HLA-B50 SLA-2050 HLA-B580 HLA-A00 SLA-2040 SLA-070

62 BoLA-N0340 BoLA-N0402 BoLA-N090 BoLA-N040 BoLA-T2c BoLA-N0030 BoLA-N0260 BoLA-N0490 BoLA-N0302 BoLA-HD6 BoLA-N0502 BoLA-N050 BoLA-N0500 BoLA-N0370 BoLA-N0460 BoLA-N050 BoLA-N0430 BoLA-N0470 BoLA-NC0040 BoLA-N00802 BoLA-N0080 BoLA-N0480 BoLA-N030 BoLA-D8.4 BoLA-T5 BoLA-N0090 BoLA-N0240 BoLA-N02402 BoLA-N0220 BoLA-T2a BoLA-N0450 BoLA-N0440 BoLA-N0550 BoLA-N080 BoLA-N0300 BoLA-N000 BoLA-N00602 BoLA-N0060 BoLA-N060 BoLA-N0250 BoLA-NC4000 BoLA-NC40020 BoLA-NC3000 BoLA-N00 BoLA-N0400 BoLA-N0390 BoLA-AW0 BoLA-N0002 BoLA-N0003 BoLA-JSP. BoLA-N0350 BoLA-N070 BoLA-N0702 BoLA-N0380 BoLA-N0270 BoLA-N0040 BoLA-N0560 BoLA-N0540 BoLA-T7 BoLA-N0520 BoLA-N0360 BoLA-NC2000 BoLA-N0050 BoLA-N0200 BoLA-N0420 BoLA-N0290 BoLA-N0280 BoLA-N020 BoLA-T2b BoLA

63 HLA-B30 BoLA-N040 HLA-B400 BoLA-N090 HLA-B4002 HLA-B490 BoLA-N0402 BoLA-N0200 HLA-B450 HLA-B4402 BoLA-N0420 HLA-B2705 HLA-A300 BoLA-N0550 BoLA-T2a HLA-A0 HLA-A030 BoLA-N0440 BoLA-N0450 HLA-A680 HLA-A30 HLA-A3303 HLA-B080 HLA-B50 HLA-B560 BoLA-N0060 BoLA-N00602 BoLA-N0250 BoLA-N060 HLA-B0702 BoLA-N000 BoLA-N0300 HLA-B350 BoLA-N080 BoLA-N0702 BoLA-N070 BoLA-N0350 BoLA-JSP. BoLA-N0002 BoLA-AW0 BoLA-N0003 BoLA-N0390 HLA-A2402 HLA-A230 BoLA-NC3000 BoLA-N0400 BoLA-NC4000 BoLA-NC40020 BoLA-N00 HLA-B580 HLA-B570 BoLA-N0540 BoLA-N0560 HLA-A00 BoLA-N0480 BoLA-N0240 BoLA-N02402 BoLA-N0220 BoLA-D8.4 BoLA-T5 BoLA-N030 HLA-B50 BoLA-N0090 BoLA-NC0040 BoLA-NC000 BoLA-N0470 HLA-A3002 HLA-A800 HLA-A2902 BoLA-N0430 BoLA-N0460 BoLA-N050 HLA-B460 BoLA-N00802 BoLA-N0080 BoLA-N0370 BoLA-N0500 BoLA-N0040 BoLA-N0270 BoLA-N0380 BoLA-T7 BoLA-N0520 BoLA-N0360 BoLA-NC2000 HLA-A0206 HLA-A0204 HLA-A020 HLA-A6802 BoLA-N0050 HLA-A260 HLA-A340 BoLA-N0030 HLA-B480 BoLA-HD6 BoLA-N0302 BoLA-N050 BoLA-N0502 BoLA-N0260 BoLA-N0490 BoLA-T2c HLA-B3905 HLA-B390 HLA-B3906 BoLA-N0280 BoLA-N020 BoLA-N0290 BoLA-N0340 BoLA-T2b BoLA+Human D8.4 JSP. T7 A020 B8

64 BoLA+Human (MHCcluster-2.0)

65 What defines a T cell epitope? MHC binding Processing (Proteasomal cleavage,tap) ( ) Other proteases T cell repertoire (similarity to self) and cross-reactivity ( ) MHC:peptide complex stability Source protein abundance, cellular location and function)

66 But, can we find the haystack?

67 MTB (mycobacterium tuberculosis) Bacterial genome coding for more then 4000 proteins 700 known epitopes, found in only 30 proteins (ORFs) TB W epitopes

68 MTB (mycobacterium tuberculosis) Bacterial genome coding for more then 4000 proteins 700 known epitopes, found in only 30 proteins (ORFs) Is this biology, or history? More than unique 9mer peptides Where to start? Each HLA allele will binding ~5000 of these peptides..

69 Functional bias in TB epitope proteins Tang et al. J Immunol. 20 Jan 5;86(2):

70 Functional bias in TB epitope proteins All selections equally good, except for conservation Tang et al. J Immunol. 20 Jan 5;86(2):

71 CBS immunology web servers

72 Acknowledgements Immunological Bioinformatics group, CBS, DTU Ole Lund - Group leader Claus Lundegaard - Data bases, HLA binding predictions Collaborators IMMI, University of Copenhagen Søren Buus: MHC binding La Jolla Institute of Allergy and Infectious Diseases A. Sette, B. Peters: Epitope database Karolinska Institutet, Stockholm Annika Karlsson, HIV data and many, many more

73 And some of the people who really did the work Ilka Hoof Nicolas Rapin Hao Zhang Mette Voldby Larsen Thomas Stranzl Massimo Andreatta Leon Jessen Edita Karosiene Martin Thomsen Jens Kringelum