, Volume 59, Issue 1, pp 25–35

A modular concept of HLA for comprehensive peptide binding prediction

  • David S. DeLuca
  • Barbara Khattab
  • Rainer Blasczyk
Original Paper


A variety of algorithms have been successful in predicting human leukocyte antigen (HLA)-peptide binding for HLA variants for which plentiful experimental binding data exist. Although predicting binding for only the most common HLA variants may provide sufficient population coverage for vaccine design, successful prediction for as many HLA variants as possible is necessary to understand the immune response in transplantation and immunotherapy. However, the high cost of obtaining peptide binding data limits the acquisition of binding data. Therefore, a prediction algorithm, which applies the binding information from well-studied HLA variants to HLA variants, for which no peptide data exist, is necessary. To this end, a modular concept of class I HLA-peptide binding prediction was developed. Accurate predictions were made for several alleles without using experimental peptide binding data specific to those alleles. We include a comparison of module-based prediction and supertype-based prediction. The modular concept increased the number of predictable alleles from 15 to 75 of HLA-A and 12 to 36 of HLA-B proteins. Under the modular concept, binding data of certain HLA alleles can make prediction possible for numerous additional alleles. We report here a ranking of HLA alleles, which have been identified to be the most informative. Modular peptide binding prediction is freely available to researchers on the web at


Histocompatibility Antigens class I Variation (genetics)/immunology 



major histocompatibility complex binding database


area under the receiver operating characteristic curve






true positive


true negative


false positive


false negative

P1, P2, ..., P9

portions of the HLA binding groove responsible for binding positions 1, 2, ..., 9 of the peptide

Supplementary material

251_2006_176_MOESM1_ESM.pdf (103 kb)
Supplemental figureShannon’s entropy is a measure variance in a system, and is applied here to the positions in-cluded in HLA pocket definitions. Average entropy was determined by calculating the en-tropy for each position in the pocket definition, and dividing by the number of positions in the pocket definition. Num. High Entropy Positions refers to the number of positions in the pocket definition that had entropy above the threshold of 2.4. Entropy values above 2.0 are considered variable (Reche et al. 2004) (PDF 105 kb)
251_2006_176_MOESM2_ESM.pdf (68 kb)
Supplementary Table 1Ranking of the alleles which would provide the most new module data (PDF 69 kb)
251_2006_176_MOESM3_ESM.pdf (24 kb)
Supplementary Table 2Ranking of the alleles which would provide the most new anchor module data (PDF 24 kb)
251_2006_176_MOESM4_ESM.pdf (24 kb)
Supplementary Table 3Ranking of the alleles which would enable the prediction of the most (PDF 24 kb)
251_2006_176_MOESM5_ESM.pdf (113 kb)
Supplementary Table 4Peptide sequences derived from random positions in a random selection of human proteins (PDF 115 kb)


  1. Bade-Doeding C, Elsner HA, Eiz-Vesper B, Seltsam A, Holtkamp U, Blasczyk R (2004) A single amino-acid polymorphism in pocket A of HLA-A*6602 alters the auxiliary anchors compared with HLA-A*6601 ligands. Immunogenetics 56:83–88PubMedCrossRefGoogle Scholar
  2. Bade-Doeding C, Eiz-Vesper B, Figueiredo C, Seltsam A, Elsner HA, Blasczyk R (2005) Peptide-binding motif of HLA-A*6603. Immunogenetics 56:769–72PubMedCrossRefGoogle Scholar
  3. Bhasin M, Raghava GP (2004) Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13:596–607PubMedCrossRefGoogle Scholar
  4. Bhasin M, Singh H, Raghava GP (2003) MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:665–666PubMedCrossRefGoogle Scholar
  5. Buus S, Lauemoller SL, Worning P, Kesmir C, Frimurer T, Corbet S, Fomsgaard A, Hilden J, Holm A, Brunak S (2003) Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 62:378–384PubMedCrossRefGoogle Scholar
  6. Chelvanayagam G (1996) A roadmap for HLA-A HLA-B and HLA-C peptide binding specificities. Immunogenetics 45:15–26PubMedCrossRefGoogle Scholar
  7. Davies MN, Sansom CE, Beazley C, Moss DS (2003) A novel predictive technique for the MHC class II peptide-binding interaction. Mol Med 9:220–225PubMedGoogle Scholar
  8. Donnes P, Elofsson A (2002) Prediction of MHC class I binding peptides using SVMHC. BMC Bioinformatics 3:25PubMedCrossRefGoogle Scholar
  9. Donnes P, Kohlbacher O (2005) Integrated modeling of the major events in the MHC class I antigen processing pathway. Protein Sci 14:2132–2140PubMedCrossRefGoogle Scholar
  10. Doytchinova IA, Flower DR (2006) Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage TAP affinity and MHC binding. Mol Immunol 43:2037–2044PubMedCrossRefGoogle Scholar
  11. Gotch F, McMichael A, Rothbard J (1988) Recognition of influenza A matrix protein by HLA-A2-restricted cytotoxic T lymphocytes. Use of analogues to orientate the matrix peptide in the HLA-A2 binding site. J Exp Med 168:2045–2057PubMedCrossRefGoogle Scholar
  12. Goulmy E, Schipper R, Pool J, Blokland E, Falkenburg JH, Vossen J, Gratwohl A, Vogelsang GB, van Houwelingen HC, van Rood JJ (1996) Mismatches of minor histocompatibility antigens between HLA-identical donors and recipients and the development of graft-versus-host disease after bone marrow transplantation. N Engl J Med 334:281–285PubMedCrossRefGoogle Scholar
  13. Guan P, Hattotuwagama CK, Doytchinova IA, Flower DR (2006) MHCPred 2.0: an updated quantitative T-cell epitope prediction server. Appl Bioinformatics 5:55–61PubMedCrossRefGoogle Scholar
  14. Hambach L, Goulmy E (2005) Immunotherapy of cancer through targeting of minor histocompatibility antigens. Curr Opin Immunol 17:202–210PubMedCrossRefGoogle Scholar
  15. Kotsch K, Blasczyk R (2000) The noncoding regions of HLA-DRB uncover interlineage recombinations as a mechanism of HLA diversification. J Immunol 165:5664–5670PubMedGoogle Scholar
  16. Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S, Lund O, Nielsen M (2005) An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding TAP transport efficiency and proteasomal cleavage predictions. Eur J Immunol 35:2295–2303PubMedCrossRefGoogle Scholar
  17. Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017PubMedCrossRefGoogle Scholar
  18. Noguchi H, Kato R, Hanai T, Matsubara Y, Honda H, Brusic V, Kobayashi T (2002) Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng 94:264–270PubMedCrossRefGoogle Scholar
  19. Parker KC, Bednarek MA, Coligan JE (1994) Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 152:163–75PubMedGoogle Scholar
  20. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219PubMedCrossRefGoogle Scholar
  21. Reche PA, Reinherz EL (2003) Sequence variability analysis of human class I and class II MHC molecules: functional and structural correlates of amino acid polymorphisms. J Mol Biol 331:623–641PubMedCrossRefGoogle Scholar
  22. Reche PA, Glutting JP, Zhang H, Reinherz EL (2004) Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles. Immunogenetics 56:405–419PubMedCrossRefGoogle Scholar
  23. Robinson J, Waller MJ, Parham P, de Groot N, Bontrop R, Kennedy LJ, Stoehr P, Marsh SG (2003) IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex. Nucleic Acids Res 31:311–314PubMedCrossRefGoogle Scholar
  24. Rognan D, Scapozza L, Folkers G, Daser A (1994) Molecular dynamics simulation of MHC–peptide complexes as a tool for predicting potential T cell epitopes. Biochemistry 33:11476–11486PubMedCrossRefGoogle Scholar
  25. Rothbard JB (1992) Synthetic peptides as vaccines. Biotechnology 20:451–465PubMedGoogle Scholar
  26. Sette A, Sidney J (1999) Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 50:201–212PubMedCrossRefGoogle Scholar
  27. Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17:555–561PubMedCrossRefGoogle Scholar
  28. Tenzer S, Peters B, Bulik S, Schoor O, Lemmel C, Schatz MM, Kloetzel PM, Rammensee HG, Schild H, Holzhutter HG (2005) Modeling the MHC class I pathway by combining predictions of proteasomal cleavage TAP transport and MHC class I binding. Cell Mol Life Sci 62:1025–1037PubMedCrossRefGoogle Scholar
  29. Yanover C, Hertz T (2005) Predicting protein–peptide binding affinity by learning peptide–peptide distance functions. Lect Notes Comput Sci 3500:456–471CrossRefGoogle Scholar
  30. Yewdell JW, Bennink JR (1999) Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu Rev Immunol 17:51–88PubMedCrossRefGoogle Scholar
  31. Zhang GL, Petrovsky N, Kwoh CK, August JT, Brusic V (2006) PREDTAP: a system for prediction of peptide binding to the human transporter associated with antigen processing. Immunome Res 2:3PubMedCrossRefGoogle Scholar
  32. Zhu S, Udaka K, Sidney J, Sette A, Aoki-Kinoshita KF, Mamitsuka H (2006) Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules. Bioinformatics 22:1648–55PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • David S. DeLuca
    • 1
  • Barbara Khattab
    • 1
  • Rainer Blasczyk
    • 1
  1. 1.Institute for Transfusion MedicineHanover Medical SchoolHanoverGermany

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