Immunogenetics

, Volume 56, Issue 6, pp 405–419 | Cite as

Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

  • Pedro A. Reche
  • John-Paul Glutting
  • Hong Zhang
  • Ellis L. Reinherz
Original Paper

Abstract

We introduced previously an on-line resource, RANKPEP that uses position specific scoring matrices (PSSMs) or profiles for the prediction of peptide-MHC class I (MHCI) binding as a basis for CD8 T-cell epitope identification. Here, using PSSMs that are structurally consistent with the binding mode of MHC class II (MHCII) ligands, we have extended RANKPEP to prediction of peptide-MHCII binding and anticipation of CD4 T-cell epitopes. Currently, 88 and 50 different MHCI and MHCII molecules, respectively, can be targeted for peptide binding predictions in RANKPEP. Because appropriate processing of antigenic peptides must occur prior to major histocompatibility complex (MHC) binding, cleavage site prediction methods are important adjuncts for T-cell epitope discovery. Given that the C-terminus of most MHCI-restricted epitopes results from proteasomal cleavage, we have modeled the cleavage site from known MHCI-restricted epitopes using statistical language models. The RANKPEP server now determines whether the C-terminus of any predicted MHCI ligand may result from such proteasomal cleavage. Also implemented is a variability masking function. This feature focuses prediction on conserved rather than highly variable protein segments encoded by infectious genomes, thereby offering identification of invariant T-cell epitopes to thwart mutation as an immune evasion mechanism.

Keywords

Epitopes Major histocompatibility complex Prediction Profile Proteasome 

References

  1. Adams HP, Koziol JA (1995) Prediction of binding to MHC class I molecules. J Immunol Methods 185:181–190CrossRefPubMedGoogle Scholar
  2. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402PubMedGoogle Scholar
  3. Altuvia Y, Margalit H (2000) Sequence signals for generation of antigenic peptides by the proteasome: implications for proteasomal cleavage mechanism. J Mol Biol 295:879–890CrossRefPubMedGoogle Scholar
  4. Altuvia Y, Sette A, Sidney J, Southwood S, Margalit H (1997) A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum Immunol 58:1–11CrossRefPubMedGoogle Scholar
  5. Bailey TL, Elkan C (1995) The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol 3:21–29PubMedGoogle Scholar
  6. Barber LD, Parham P (1993) Peptide binding to major histocompatibility complex molecules. Annu Rev Cell Biol 9:163–206PubMedGoogle Scholar
  7. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2003) GenBank. Nucleic Acids Res 31:23–27CrossRefPubMedGoogle Scholar
  8. Brusic V, Rudy G, Honeyman JH, Harrison LC (1998a) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neuronal network. Bioinformatics 14:121–130CrossRefPubMedGoogle Scholar
  9. Brusic V, Rudy G, Kyne AP, Harrison LC (1998b) MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res 26:368–371CrossRefPubMedGoogle Scholar
  10. Carson RT, Vignali KM, Woodland DL, Vignali DA (1997) T-cell receptor recognition of MHC class II-bound peptide flanking residues enhances immunogenicity and results in altered TCR V region usage. Immunity 7:387–399CrossRefPubMedGoogle Scholar
  11. Chen W, Norbury CC, Cho Y, Yewdell JW, Bennink JR (2001) Immunoproteasomes shape immunodominance hierarchies of antiviral CD8(+) T-cells at the levels of T-cell repertoire and presentation of viral antigens. J Exp Med 193:1319–1326CrossRefPubMedGoogle Scholar
  12. Craiu A, Akopian T, Goldberg A, Rock KL (1997) Two distinct proteolytic processes in the generation of a major histocompatibility complex class I-presented peptide. Proc Natl Acad Sci USA 94:10850–10855CrossRefPubMedGoogle Scholar
  13. De Groot AS, Jesdale BM, Szu E, Schafer JR, Chicz RM, Deocampo G (1997) An interactive web site providing major histocompatibility ligand predictions: application to HIV research and AIDS. AIDS Res Hum Retroviruses 13:529–531PubMedGoogle Scholar
  14. Donnes P, Elofsson A (2002) Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinform 529–531:25CrossRefGoogle Scholar
  15. Draenert R, Altfeld M, Brander C, Basgoz N, Corcoran C, Wurcel AG, Stone DR, Kalams SA, Trocha A, Addo MM, Goulder PJ, Walker BD (2003) Comparison of overlapping peptide sets for detection of antiviral CD8 and CD4 T-cell responses. J Immunol Methods 275:19–29CrossRefPubMedGoogle Scholar
  16. Falk K, Rotzschke O, Stevanovic S, Jung G, Rammensee HG (1991) Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351:290–296CrossRefPubMedGoogle Scholar
  17. Fruh K, Yang Y (1999) Antigen presentation by MHC class I and its regulation by interferon gamma. Curr Opin Immunol 11:76–81CrossRefPubMedGoogle Scholar
  18. Garcia KC, Teyton L, Wilson IA (1999) Structural basis of T-cell recognition. Annu Rev Immunol 17:369–397CrossRefPubMedGoogle Scholar
  19. Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci USA 84:4355–4358PubMedGoogle Scholar
  20. Guan P, Doytchinova IA, Zygouri C, Flower DR (2003) MHCPred: a server for quantitative prediction of peptide-MHC binding. Nucleic Acids Res 31:3621–3624CrossRefPubMedGoogle Scholar
  21. Gulukota K, Sidney J, Sette A, DeLisi C (1997) Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J Mol Biol 267:1258–1267CrossRefPubMedGoogle Scholar
  22. van Hall T, Sijts A, Camps M, Offringa R, Melief C, Kloetzel PM, Ossendorp F (2000) Differential influence on cytotoxic T lymphocyte epitope presentation by controlled expression of either proteasome immunosubunits or PA28. J Exp Med 192:483–494CrossRefPubMedGoogle Scholar
  23. Hammer J (1995) New methods to predict MHC-binding sequences within protein antigens. Curr Opin Immunol 7:263–269CrossRefPubMedGoogle Scholar
  24. Hammer J, Bono E, Gallazzi F, Belunis C, Nagy Z, Sinigaglia F (1994) Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J Exp Med 267:1258–1267Google Scholar
  25. Henikoff S, Henikoff JG (1994) Position-based sequence weights. J Mol Biol 243:574–578CrossRefPubMedGoogle Scholar
  26. Henikoff JG, Henikoff S (1996) Using substitution probabilities to improve position-specific scoring matrices. Comput Appl Biosci 12:135–143PubMedGoogle Scholar
  27. Henikoff S, Henikoff JG, Pietrokovski S (1999) Blocks+: a non-redundant database of protein alignment blocks derived from multiple compilations. Bioinformatics 15:471–479CrossRefPubMedGoogle Scholar
  28. Hennecke J, Carfi A, Wiley DC (2000) Structure of a covalently stabilized complex of a human alphabeta T-cell receptor, influenza HA peptide and MHC class II molecule, HLA-DR1. EMBO J 19:5611–5624CrossRefPubMedGoogle Scholar
  29. Hofmann K, Bucher P, Falquet L, Bairoch A (1999) The PROSITE database, its status in 1999. Nucleic Acids Res 27:215–219CrossRefPubMedGoogle Scholar
  30. Holzhutter HG, Kloetzel PM (2000) A kinetic model of vertebrate 20S proteasome accounting for the generation of major proteolytic fragments from oligomeric peptide substrates. Biophys J 79:1196–1205PubMedGoogle Scholar
  31. Honeyman MC, Brusic V, Stone NL, Harrison LC (1998) Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol 16:966–969CrossRefPubMedGoogle Scholar
  32. Jimenez-Montano MA, Ebeling W, Pohl T, Rapp PE (2002) Entropy and complexity of finite sequences as fluctuating quantities. Biosystems 64:23–32CrossRefPubMedGoogle Scholar
  33. Kesmir C, Nussbaum AK, Schild H, Detours V, Brunak S (2002) Prediction of proteasome cleavage motifs by neural networks. Protein Eng 15:287–296CrossRefPubMedGoogle Scholar
  34. Kisselev AF, Akopian TN, Woo KM, Goldberg AL (1999) The sizes of peptides generated from protein by mammalian 26 and 20 S proteasomes. Implications for understanding the degradative mechanism and antigen presentation. J Biol Chem 274:3363–3371CrossRefPubMedGoogle Scholar
  35. Kuttler C, Nussbaum AK, Dick TP, Rammensee HG, Schild H, Hadeler KP (2000) An algorithm for the prediction of proteasomal cleavages. J Mol Biol 298:417–429CrossRefPubMedGoogle Scholar
  36. Madden DR (1995) The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol 13:587–622CrossRefPubMedGoogle Scholar
  37. Madden DR, Garboczi DN, Wiley DC (1993) The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell 75:693–708CrossRefPubMedGoogle Scholar
  38. Maenaka K, Jones EY (1999) MHC superfamily structure and the immune system. Curr Opin Struct Biol 9:745–753CrossRefPubMedGoogle Scholar
  39. Mallios RR (1999) Class II MHC quantitative binding motifs derived from a large molecular database with a versatile iterative stepwise discriminant analysis meta-algorithm. Bioinformatics 15:432–439CrossRefPubMedGoogle Scholar
  40. Mamitsuka H (1998) Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:460–474CrossRefPubMedGoogle Scholar
  41. Margulies DH (1997) Interactions of TCRs with MHC-peptide complexes: a quantitative basis for mechanistic models. Curr Opin Immunol 9:390–395CrossRefPubMedGoogle Scholar
  42. Matsumura M, Fremont D, Peterson PA, Wilson IA (1992) Emerging principles for the recognition of peptide antigens by MHC class I molecules. Science 257:927–934PubMedGoogle Scholar
  43. Meister GE, Roberts CG, Berzofsky JA, De Groot AS (1995) Two novel T-cell epitope prediction algorithms based on MHC-binding motifs; comparison of predicted and published epitopes from Mycobacterium tuberculosis and HIV protein sequences. Vaccine 13:581–591CrossRefPubMedGoogle Scholar
  44. Nicholls A, Sharp K, Honig B (1991) Protein folding and association insights from the interfacial and thermodynamic properties of hydrocarbons. Proteins 11:281–296PubMedGoogle Scholar
  45. Pamer E, Cresswell P (1998) Mechanisms of MHC class I—restricted antigen processing. Annu Rev Immunol 16:323–358CrossRefPubMedGoogle Scholar
  46. 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 152:163–175PubMedGoogle Scholar
  47. Peters B, Tong W, Sidney J, Sette A, Weng Z (2003) Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules. Bioinformatics 19:1765–1772CrossRefPubMedGoogle Scholar
  48. Pieters J (2000) MHC class II-restricted antigen processing and presentation. Adv Immunol 75:159–208CrossRefPubMedGoogle Scholar
  49. Raddrizzani L, Hammer J (2000) Epitope scanning using virtual matrix-based algorithms. Brief Bioinform 1:179–189PubMedGoogle Scholar
  50. Rammensee HG (2002) Survival of the fitters. Nature 419:443–445CrossRefPubMedGoogle Scholar
  51. Rammensee HG, Friede T, Stevanoviic S (1995) MHC ligands and peptide motifs: first listing. Immunogenetics 41:178–228CrossRefPubMedGoogle Scholar
  52. Rammensee HG, Bachmann J, Emmerich NPN, Bacho OA, Stevanovic S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219CrossRefPubMedGoogle Scholar
  53. 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 331:623–641CrossRefPubMedGoogle Scholar
  54. Reche PA, Glutting JP, Reinherz EL (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709CrossRefPubMedGoogle Scholar
  55. Rosenfeld R (2000) Two decades of statistical language modeling: where do we go from here? Proc IEEE 88:1–11CrossRefGoogle Scholar
  56. Sant’Angelo DB, Robinson E, Janeway CA Jr, Denzin LK (2002) Recognition of core and flanking amino acids of MHC class II-bound peptides by the T-cell receptor. Eur J Immunol 32:2510–2520CrossRefPubMedGoogle Scholar
  57. Schaffer AA, Wolf YI, Ponting CP, Koonin EV, Aravind L, Altschul SF (1999) IMPALA: matching a protein sequence against a collection of PSI-BLAST-constructed position-specific score matrices. Bioinformatics 15:1000–1011CrossRefPubMedGoogle Scholar
  58. Schneider TD, Stephens RM (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–6100PubMedGoogle Scholar
  59. Schueler-Furman O, Altuvia Y, Sette A, Margalit H (2000) Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 9:1838–1846PubMedGoogle Scholar
  60. Serwold T, Gonzalez F, Kim J, Jacob R, Shastri N (2002) ERAAP customizes peptides for MHC class I molecules in the endoplasmic reticulum. Nature 419:480–483CrossRefPubMedGoogle Scholar
  61. Sette A, Buus S, Appella E, Smith JA, Chesnut R, Miles C, Colon SM, Grey HM (1989) Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc Natl Acad Sci USA 86:3296–3300PubMedGoogle Scholar
  62. Shannon CE (1948) The mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656Google Scholar
  63. Stern LJ, Wiley DC (1994) Antigen peptide binding by class I and class II histocompatibility proteins. Structure 2:245–251CrossRefPubMedGoogle Scholar
  64. Stewart JJ, Lee CY, Ibrahim S, Watts P, Shlomchik M, Weigert M, Litwin S (1997) A Shannon entropy analysis of immunoglobulin and T-cell receptor. Mol Immunol 34:1067–1082CrossRefPubMedGoogle Scholar
  65. Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: Ohala TMNJJ, Derwing BL, Hodge MM, Wiebe GE (eds) Proceedings of the International Conference of Spoken Language Processing. Center for Spoken Language Research, Boulder, pp 901–904Google Scholar
  66. Stryhn A, Pederson LO, Romme T, Holm A, Buus S (1996) Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding. Eur J Immunol 26:1911–1918PubMedGoogle Scholar
  67. Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nature Biotech 17:555–561Google Scholar
  68. Swain MT, Brooks AJ, Kemp GJL (2001) An automated approach to modelling class II MHC alleles and predicting peptide binding. Proceedings of the IEEE International Symposium on Bio-Informatics and Biomedical Engineering. IEEE Computer Society, New York, pp 81–88Google Scholar
  69. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293PubMedGoogle Scholar
  70. Thompson JD, Higgins DG, Gibson TJ (1994a) CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weigh matrix choice. Nucleic Acids Res 2:4673–4680Google Scholar
  71. Thompson JD, Higgins DG, Gibson TJ (1994b) Improved sensitivity of profile searches through the use of sequence weights and gap excision. Comput Appl Biosci 10:19–29PubMedGoogle Scholar
  72. Toes RE, Nussbaum AK, Degermann S, Schirle M, Emmerich NP, Kraft M, Laplace C, Zwinderman A, Dick TP, Muller J, Schonfisch B, Schmid C, Fehling HJ, Stevanovic S, Rammensee HG, Schild H (2001) Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J Exp Med 194:1–12CrossRefPubMedGoogle Scholar
  73. Udaka K, Wiesmuller KH, Kienle S, Jung G, Tamamura H, Yamigishi H, Okumura K, Walden P, Suto T, Kawasaki T (2000) An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries. Immunogenetics 51:816–828CrossRefPubMedGoogle Scholar
  74. Udaka K, Mamitsuka H, Nakaseko Y, Abe N (2002) Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides. J Immunol 169:5744–5753PubMedGoogle Scholar
  75. Wang J-H, Reinherz E (2001) Structural basis of T-cell recognition of peptides bound to MHC molecules. Mol Immunol 38:1039–1049CrossRefGoogle Scholar
  76. Watts C (2001) Antigen processing in the endocytic compartment. Curr Opin Immunol 13:26–31CrossRefPubMedGoogle Scholar
  77. Wu C, Shivakumar S (1994) Back-propagation and counter-propagation neural networks for phylogenetic classification of ribosomal RNA sequences. Nucleic Acids Res 22:4291–4299PubMedGoogle Scholar
  78. Wu CH, Zhao S, Chen HL, Lo CJ, McLarty J (1996) Motif identification neural design for rapid and sensitive protein family search. Comput Appl Biosci 12:109–118PubMedGoogle Scholar
  79. Zhang C, Anderson A, DeLisi C (1998) Structural principles that govern the peptide-binding motifs of class I MHC molecules. J Mol Biol 281:929–947CrossRefPubMedGoogle Scholar
  80. Zhao Y, Pinilla C, Valmori D, Martin R, Simon R (2003) Application of support vector machines for T-cell epitopes prediction. Bioinformatics 19:1978–1984CrossRefPubMedGoogle Scholar
  81. Zhong W, Reche PA, Lai CC, Reinhold B, Reinherz EL (2003) Genome-wide characterization of a viral cytotoxic T lymphocyte epitope repertoire. J Biol Chem 278:45135–45144CrossRefPubMedGoogle Scholar
  82. Zinkernagel RM, Doherty PC (1974) Restriction of in vitro T-cell-mediated cytotoxicity in lymphocytic choriomeningitis within a syngeneic or semiallogeneic system. Nature 248:701–702PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Pedro A. Reche
    • 1
    • 2
  • John-Paul Glutting
    • 1
  • Hong Zhang
    • 1
  • Ellis L. Reinherz
    • 1
    • 2
  1. 1.Laboratory of Immunobiology and Department of Medical OncologyDana-Farber Cancer InstituteBostonUSA
  2. 2.Department of MedicineHarvard Medical SchoolBostonUSA

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