1932

Abstract

Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide–MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-021920-100259
2020-07-20
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/3/1/annurev-biodatasci-021920-100259.html?itemId=/content/journals/10.1146/annurev-biodatasci-021920-100259&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Lund O, Nielsen M, Lundegaard C, Kesmir C, Brunak S 2005. Immunological Bioinformatics Cambridge, MA: MIT Press
    [Google Scholar]
  2. 2. 
    Davis MM, Krogsgaard M, Huse M, Huppa J, Lillemeier BF, Li Q 2007. T cells as a self-referential, sensory organ. Annu. Rev. Immunol. 25:681–95
    [Google Scholar]
  3. 3. 
    Rock KL, Reits E, Neefjes J 2016. Present yourself! By MHC class I and MHC class II molecules. Trends Immunol 37:11724–37
    [Google Scholar]
  4. 4. 
    Kobayashi KS, van den Elsen PJ 2012. NLRC5: a key regulator of MHC class I-dependent immune responses. Nat. Rev. Immunol. 12:12813–20
    [Google Scholar]
  5. 5. 
    Wieczorek M, Abualrous ET, Sticht J, Álvaro-Benito M, Stolzenberg S et al. 2017. Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front. Immunol. 8:292
    [Google Scholar]
  6. 6. 
    Thorsby E. 2009. A short history of HLA. Tissue Antigens 74:2101–16
    [Google Scholar]
  7. 7. 
    Levine BB, Ojeda A, Benacerraf B 1963. Studies on artificial antigens. III. The genetic control of the immune response to hapten-poly-l-lysine conjugates in guinea pigs. J. Exp. Med. 118:953–57
    [Google Scholar]
  8. 8. 
    McDevitt H. 2002. The discovery of linkage between the MHC and genetic control of the immune response. Immunol. Rev. 185:78–85
    [Google Scholar]
  9. 9. 
    Zinkernagel RM, Doherty PC. 1997. The discovery of MHC restriction. Immunol. Today 18:114–17
    [Google Scholar]
  10. 10. 
    Benacerraf B. 1978. A hypothesis to relate the specificity of T lymphocytes and the activity of I region-specific Ir genes in macrophages and B lymphocytes. J. Immunol. 120:61809–12
    [Google Scholar]
  11. 11. 
    Rosenthal AS. 1978. Determinant selection and macrophage function in genetic control of the immune response. Immunol. Rev. 40:136–52
    [Google Scholar]
  12. 12. 
    Web of Stories 2017. Jan Klein: period of confusion in immunology with many false claims. Interview, Aug. 3. https://www.youtube.com/watch?v=dC7Cy926u_s
  13. 13. 
    Shimonkevitz R, Colon S, Kappler JW, Marrack P, Grey HM 1984. Antigen recognition by H-2-restricted T cells. II. A tryptic ovalbumin peptide that substitutes for processed antigen. J. Immunol. 133:42067–74
    [Google Scholar]
  14. 14. 
    Townsend AR, Gotch FM, Davey J 1985. Cytotoxic T cells recognize fragments of the influenza nucleoprotein. Cell 42:2457–67
    [Google Scholar]
  15. 15. 
    Watts TH, Brian AA, Kappler JW, Marrack P, McConnell HM 1984. Antigen presentation by supported planar membranes containing affinity-purified I-Ad. PNAS 81:237564–68
    [Google Scholar]
  16. 16. 
    Dembić Z, Haas W, Weiss S, McCubrey J, Kiefer H et al. 1986. Transfer of specificity by murine alpha and beta T-cell receptor genes. Nature 320:6059232–38
    [Google Scholar]
  17. 17. 
    Babbitt BP, Allen PM, Matsueda G, Haber E, Unanue ER 1985. Binding of immunogenic peptides to Ia histocompatibility molecules. Nature 317:6035359–61
    [Google Scholar]
  18. 18. 
    Buus S, Sette A, Colon SM, Jenis DM, Grey HM 1986. Isolation and characterization of antigen-Ia complexes involved in T cell recognition. Cell 47:61071–77
    [Google Scholar]
  19. 19. 
    Buus S, Sette A, Colon SM, Miles C, Grey HM 1987. The relation between major histocompatibility complex (MHC) restriction and the capacity of Ia to bind immunogenic peptides. Science 235:47941353–58
    [Google Scholar]
  20. 20. 
    Sette A, Buus S, Colon S, Smith JA, Miles C, Grey HM 1987. Structural characteristics of an antigen required for its interaction with Ia and recognition by T cells. Nature 328:6129395–99
    [Google Scholar]
  21. 21. 
    Sette A, Buus S, Colon S, Miles C, Grey HM 1988. I-Ad-binding peptides derived from unrelated protein antigens share a common structural motif. J. Immunol. 141:145–48
    [Google Scholar]
  22. 22. 
    Buus S, Sette A, Grey HM 1987. The interaction between protein-derived immunogenic peptides and Ia. Immunol. Rev. 98:115–41
    [Google Scholar]
  23. 23. 
    Buus S, Sette A, Colon SM, Grey HM 1988. Autologous peptides constitutively occupy the antigen binding site on Ia. Science 242:48811045–47
    [Google Scholar]
  24. 24. 
    Bjorkman PJ, Saper MA, Samraoui B, Bennett WS, Strominger JL, Wiley DC 1987. Structure of the human class I histocompatibility antigen, HLA-A2. Nature 329:6139506–12
    [Google Scholar]
  25. 25. 
    Bjorkman PJ, Saper MA, Samraoui B, Bennett WS, Strominger JL, Wiley DC 1987. The foreign antigen binding site and T cell recognition regions of class I histocompatibility antigens. Nature 329:6139512–18
    [Google Scholar]
  26. 26. 
    Brown JH, Jardetzky TS, Gorga JC, Stern LJ, Urban RG et al. 1993. Three-dimensional structure of the human class II histocompatibility antigen HLA-DR1. Nature 364:643233–39
    [Google Scholar]
  27. 27. 
    Yewdell JW, Bennink JR. 1999. Immunodominance in major histocompatibility complex class I–restricted T lymphocyte responses. Annu. Rev. Immunol. 17:51–88
    [Google Scholar]
  28. 28. 
    Sidney J, Southwood S, Moore C, Oseroff C, Pinilla C et al. 2013. Measurement of MHC/peptide interactions by gel filtration or monoclonal antibody capture. Curr. Protoc. Immunol. 100:18.3.1–18.3.36
    [Google Scholar]
  29. 29. 
    Pedersen , Nissen MH, Hansen NJ, Nielsen LL, Lauenmøller SL et al. 2001. Efficient assembly of recombinant major histocompatibility complex class I molecules with preformed disulfide bonds. Eur. J. Immunol. 31:102986–96
    [Google Scholar]
  30. 30. 
    Stryhn A, Pedersen , Romme T, Holm CB, 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. 26:81911–18
    [Google Scholar]
  31. 31. 
    Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD et al. 2015. The Immune Epitope Database (IEDB) 3.0. Nucleic Acids Res 43:D405–12
    [Google Scholar]
  32. 32. 
    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 351:6324290–96
    [Google Scholar]
  33. 33. 
    Engelhard VH. 1994. Structure of peptides associated with class I and class II MHC molecules. Annu. Rev. Immunol. 12:181–207
    [Google Scholar]
  34. 34. 
    Nielsen M, Lund O, Buus S, Lundegaard C 2010. MHC class II epitope predictive algorithms. Immunology 130:3319–28
    [Google Scholar]
  35. 35. 
    Lundegaard C, Lund O, Buus S, Nielsen M 2010. Major histocompatibility complex class I binding predictions as a tool in epitope discovery. Immunology 130:3309–18
    [Google Scholar]
  36. 36. 
    Mei S, Li F, Leier A, Marquez-Lago TT, Giam K et al. 2019. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 2019.bbz051
    [Google Scholar]
  37. 37. 
    Andreatta M, Nielsen M. 2016. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32:4511–17
    [Google Scholar]
  38. 38. 
    Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M 2017. NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J. Immunol. 199:93360–68
    [Google Scholar]
  39. 39. 
    Bassani-Sternberg M, Gfeller D. 2016. Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide–HLA interactions. J. Immunol. 197:62492–99
    [Google Scholar]
  40. 40. 
    O'Donnell TJ, Rubinsteyn A, Bonsack M, Riemer AB, Laserson U, Hammerbacher J 2018. MHCflurry: open-source class I MHC binding affinity prediction. Cell Syst 7:1129–32.e4
    [Google Scholar]
  41. 41. 
    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. 152:1163–75
    [Google Scholar]
  42. 42. 
    Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–19
    [Google Scholar]
  43. 43. 
    Peters B, Sette A. 2005. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinform 6:132
    [Google Scholar]
  44. 44. 
    Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA et al. 2018. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154:3394–406
    [Google Scholar]
  45. 45. 
    Sette A, Buus S, Appella E, Smith JA, Chesnut R et al. 1989. Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. PNAS 86:93296–300
    [Google Scholar]
  46. 46. 
    Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanović S 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:3–4213–19
    [Google Scholar]
  47. 47. 
    Bui HH, Sidney J, Peters B, Sathiamurthy M, Sinichi A et al. 2005. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 57:5304–14
    [Google Scholar]
  48. 48. 
    Odunsi K, Ganesan T. 2001. Motif analysis of HLA class II molecules that determine the HPV associated risk of cervical carcinogenesis. Int. J. Mol. Med. 8:4405–12
    [Google Scholar]
  49. 49. 
    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. 180:62353–58
    [Google Scholar]
  50. 50. 
    Gulukota K, Sidney J, Sette A, DeLisi C 1997. Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J. Mol. Biol. 267:51258–67
    [Google Scholar]
  51. 51. 
    Milik M, Sauer D, Brunmark AP, Yuan L, Vitiello A et al. 1998. Application of an artificial neural network to predict specific class I MHC binding peptide sequences. Nat. Biotechnol. 16:8753–56
    [Google Scholar]
  52. 52. 
    Adams HP, Koziol JA. 1995. Prediction of binding to MHC class I molecules. J. Immunol. Methods 185:2181–90
    [Google Scholar]
  53. 53. 
    Brusic V, Rudy G, Honeyman G, Hammer J, Harrison L 1998. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14:2121–30
    [Google Scholar]
  54. 54. 
    Buus S, Lauemoller SL, Worning P, Kesmir C, Frimurer T et al. 2003. Sensitive quantitative predictions of peptide-MHC binding by a “query by committee” artificial neural network approach. Tissue Antigens 62:5378–84
    [Google Scholar]
  55. 55. 
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K et al. 2003. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:51007–17
    [Google Scholar]
  56. 56. 
    Zhang C, Bickis MG, Wu F-X, Kusalik AJ 2006. Optimally-connected hidden Markov models for predicting MHC-binding peptides. J. Bioinform. Comput. Biol. 4:5959–80
    [Google Scholar]
  57. 57. 
    Mamitsuka H. 1998. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:4460–74
    [Google Scholar]
  58. 58. 
    Doytchinova IA, Flower DR. 2001. Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J. Med. Chem. 44:223572–81
    [Google Scholar]
  59. 59. 
    Bhasin M, Singh H, Raghava GPS 2003. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:5665–66
    [Google Scholar]
  60. 60. 
    Brusic V, Rudy G, Harrison LC 1994. MHCPEP: a database of MHC-binding peptides. Nucleic Acids Res 22:173663–65
    [Google Scholar]
  61. 61. 
    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 19:141765–72
    [Google Scholar]
  62. 62. 
    Donnes P, Elofsson A. 2002. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinform 3:25
    [Google Scholar]
  63. 63. 
    Liu W, Meng X, Xu Q, Flower DR, Li T 2006. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinform 7:182
    [Google Scholar]
  64. 64. 
    Peters B, Bui HH, Frankild S, Nielson M, Lundegaard C et al. 2006. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLOS Comput. Biol. 2:6e65
    [Google Scholar]
  65. 65. 
    Madden DR. 1995. The three-dimensional structure of peptide-MHC complexes. Annu. Rev. Immunol. 13:587–622
    [Google Scholar]
  66. 66. 
    Zhang H, Wang P, Papangelopoulos N, Xu Y, Sette A et al. 2010. Limitations of ab initio predictions of peptide binding to MHC class II molecules. PLOS ONE 5:2e9272
    [Google Scholar]
  67. 67. 
    Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V 2008. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol 9:8
    [Google Scholar]
  68. 68. 
    Brown JH, Jardetzky TS, Gorga JC, Stern LJ, Urban RG et al. 2015. Three-dimensional structure of the human class II histocompatibility antigen HLA-DR1. J. Immunol 194:15–11
    [Google Scholar]
  69. 69. 
    Noguchi H, Kato R, Hanai T, Matsubara Y, Honda H et al. 2002. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J. Biosci. Bioeng. 94:3264–70
    [Google Scholar]
  70. 70. 
    Salomon J, Flower DR. 2006. Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinform 7:501
    [Google Scholar]
  71. 71. 
    Cui J, Han LY, Lin HH, Zhang HL, Tang ZQ et al. 2007. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol. Immunol. 44:5866–77
    [Google Scholar]
  72. 72. 
    Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K et al. 2004. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20:91388–97
    [Google Scholar]
  73. 73. 
    Nielsen M, Lund O. 2009. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinform 10:296
    [Google Scholar]
  74. 74. 
    Nielsen M, Andreatta M. 2017. NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions. Nucleic Acids Res 45:W1W344–49
    [Google Scholar]
  75. 75. 
    Lin HH, Zhang GL, Tongchusak S, Reinherz EL, Brusic V 2008. Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research. BMC Bioinform 9:Suppl. 12S22
    [Google Scholar]
  76. 76. 
    Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, Nielsen M 2013. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics 65:10711–24
    [Google Scholar]
  77. 77. 
    Nielsen M, Justesen S, Lund O, Lundegaard C, Buus S 2010. NetMHCIIpan-2.0—improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Res 6:9
    [Google Scholar]
  78. 78. 
    Lundegaard C, Lund O, Nielsen M 2008. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 24:111397–98
    [Google Scholar]
  79. 79. 
    Gfeller D, Guillaume P, Michaux J, Pak H-S, Daniel RT et al. 2018. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201:123705–16
    [Google Scholar]
  80. 80. 
    Trolle T, McMurtrey CP, Sidney J, Bardet W, Osborn SC et al. 2016. The length distribution of class I-restricted T cell epitopes is determined by both peptide supply and MHC allele-specific binding preference. J. Immunol. 196:41480–87
    [Google Scholar]
  81. 81. 
    Guillaume P, Picaud S, Baumgaertner P, Montandon N, Schmidt J et al. 2018. The C-terminal extension landscape of naturally presented HLA-I ligands. PNAS 115:205083–88
    [Google Scholar]
  82. 82. 
    McMurtrey C, Trolle T, Sansom T, Remesh SG, Kaever T et al. 2016. Toxoplasma gondii peptide ligands open the gate of the HLA class I binding groove. eLife 5:e12556
    [Google Scholar]
  83. 83. 
    Pymm P, Illing PT, Ramarathinam SH, O'Connor GM, Hughes VA et al. 2017. MHC-I peptides get out of the groove and enable a novel mechanism of HIV-1 escape. Nat. Struct. Mol. Biol. 24:4387–94
    [Google Scholar]
  84. 84. 
    Zhao W, Sher X. 2018. Systematically benchmarking peptide-MHC binding predictors: from synthetic to naturally processed epitopes. PLOS Comput. Biol. 14:11e1006457
    [Google Scholar]
  85. 85. 
    Alvarez B, Reynisson B, Barra C, Buus S, Ternette N et al. 2019. NNAlign_MA; MHC peptidome deconvolution for accurate MHC binding motif characterization and improved T cell epitope predictions. Mol. Cell. Proteom. 18:122459–77
    [Google Scholar]
  86. 86. 
    Paul S, Croft NP, Purcell AW, Tscharke DC, Sette A et al. 2019. Benchmarking predictions of MHC class I restricted T cell epitopes. bioRxiv 694539. https://doi.org/10.1101/694539
    [Crossref]
  87. 87. 
    Bugembe DL, Ekii AO, Ndembi N, Sewanga J, Kaleebu P, Pala P 2020. Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort. BMC Infect. Dis. 20:1172
    [Google Scholar]
  88. 88. 
    Sette A, Vitiello A, Reherman B, Fowler P, Nayersina R et al. 1994. The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. J. Immunol. 153:125586–92
    [Google Scholar]
  89. 89. 
    Southwood S, Sidney J, Kondo A, del Guercio MF, Appella E et al. 1998. Several common HLA-DR types share largely overlapping peptide binding repertoires. J. Immunol. 160:73363–73
    [Google Scholar]
  90. 90. 
    Rao X, Costa AI, van Baarle D, Kesmir C 2009. A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses. J. Immunol. 182:31526–32
    [Google Scholar]
  91. 91. 
    Schellens IMM, Hoof I, Meiring HD, Spijkers SNM, Poelen MCM et al. 2015. Comprehensive analysis of the naturally processed peptide repertoire: differences between HLA-A and B in the immunopeptidome. PLOS ONE 10:9e0136417
    [Google Scholar]
  92. 92. 
    Paul S, Weiskopf D, Angelo MA, Sidney J, Peters B, Sette A 2013. HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J. Immunol. 191:125831–39
    [Google Scholar]
  93. 93. 
    Stranzl T, Larsen MV, Lundegaard C, Nielsen M 2010. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62:6357–68
    [Google Scholar]
  94. 94. 
    Nielsen M, Andreatta M. 2016. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med 8:133
    [Google Scholar]
  95. 95. 
    Robinson J, Halliwell JA, Hayhurst JD, Flicek P, Parham P, Marsh SG 2015. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res 43:D423–31
    [Google Scholar]
  96. 96. 
    Sette A, Sidney J. 1999. Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 50:3–4201–12
    [Google Scholar]
  97. 97. 
    Lund O, Nielsen M, Kesmir C, Petersen AG, Lundegaard C et al. 2004. Definition of supertypes for HLA molecules using clustering of specificity matrices. Immunogenetics 55:12797–810
    [Google Scholar]
  98. 98. 
    Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M et al. 2007. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLOS ONE 2:8e796
    [Google Scholar]
  99. 99. 
    Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V 2005. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res 33:W172–79
    [Google Scholar]
  100. 100. 
    Jojic N, Reyes-Gomez M, Heckerman D, Kadie C, Schueler-Furman O 2006. Learning MHC I–peptide binding. Bioinformatics 22:14e227–35
    [Google Scholar]
  101. 101. 
    Jacob L, Vert JP. 2008. Efficient peptide–MHC-I binding prediction for alleles with few known binders. Bioinformatics 24:3358–66
    [Google Scholar]
  102. 102. 
    Zhang H, Lund O, Nielsen M 2009. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics 25:101293–99
    [Google Scholar]
  103. 103. 
    Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O et al. 1999. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol. 17:6555–61
    [Google Scholar]
  104. 104. 
    Bordner AJ, Mittelmann HD. 2010. MultiRTA: a simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes. BMC Bioinform 11:482
    [Google Scholar]
  105. 105. 
    Pfeifer N, Kohlbacher O. 2008. Multiple instance learning allows MHC class II epitope predictions across alleles. Proceedings of the 8th International Workshop on Algorithms in Bioinformatics KA Crandall, J Lagergren 210–21 Berlin: Springer
    [Google Scholar]
  106. 106. 
    Nielsen M, Lundegaard C, Blicher T, Peters B, Sette A et al. 2008. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLOS Comput. Biol. 4:7e1000107
    [Google Scholar]
  107. 107. 
    Zhang L, Udaka K, Mamitsuka H, Zhu S 2012. Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Brief. Bioinform 13:3350–64
    [Google Scholar]
  108. 108. 
    Trolle T, Metushi IG, Greenbaum JA, Kim Y, Sidney J et al. 2015. Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics 31:132174–81
    [Google Scholar]
  109. 109. 
    Andreatta M, Trolle T, Yan Z, Greenbaum JA, Peters B, Nielsen M 2018. An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics 34:91522–28
    [Google Scholar]
  110. 110. 
    van der Burg SH, Visseren MJ, Brandt RM, Kast WM, Melief CJ 1996. Immunogenicity of peptides bound to MHC class I molecules depends on the MHC-peptide complex stability. J. Immunol. 156:93308–14
    [Google Scholar]
  111. 111. 
    Harndahl M, Rasmussen M, Roder G, Dalgaard Pedersen I, Sørensen M et al. 2012. Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur. J. Immunol. 42:61405–16
    [Google Scholar]
  112. 112. 
    Harndahl M, Rasmussen M, Roder G, Buus S 2011. Real-time, high-throughput measurements of peptide-MHC-I dissociation using a scintillation proximity assay. J. Immunol. Methods 374:1–25–12
    [Google Scholar]
  113. 113. 
    Jørgensen KW, Rasmussen M, Buus S, Nielsen M 2013. NetMHCstab—predicting stability of peptide:MHC-I complexes; impacts for CTL epitope discovery. Immunology 141:18–26
    [Google Scholar]
  114. 114. 
    Rasmussen M, Fenoy E, Harndahl M, Kristensen AB, Nielsen IK et al. 2016. Pan-specific prediction of peptide-MHC class I complex stability, a correlate of T cell immunogenicity. J. Immunol. 197:41517–24
    [Google Scholar]
  115. 115. 
    Nielsen M, Lundegaard C, Lund O, Kesmir C 2005. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57:1–233–41
    [Google Scholar]
  116. 116. 
    Eggers M, Boes-Fabian B, Ruppert T, Kloetzel PM, Koszinowski UH 1995. The cleavage preference of the proteasome governs the yield of antigenic peptides. J. Exp. Med. 182:61865–70
    [Google Scholar]
  117. 117. 
    Peters B, Bulik S, Tampe R, Van Endert PM, Holzhütter H-G 2003. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J. Immunol. 171:41741–49
    [Google Scholar]
  118. 118. 
    Bhasin M, Raghava GPS. 2004. Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13:3596–607
    [Google Scholar]
  119. 119. 
    Larsen MV, Lundegaard C, Lamberth K, Buus S, Brunak S et al. 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. 35:82295–303
    [Google Scholar]
  120. 120. 
    Tenzer S, Peters B, Bulik S, Schoor O, Lemmel C et al. 2005. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol. Life Sci. 62:91025–37
    [Google Scholar]
  121. 121. 
    Nussbaum AK, Dick TP, Keilholz W, Schirle M, Stevanović S et al. 1998. Cleavage motifs of the yeast 20S proteasome β subunits deduced from digests of enolase 1. PNAS 95:2112504–9
    [Google Scholar]
  122. 122. 
    Toes RE, Nussbaum AK, Degermann S, Schirle M, Emmerich NP et al. 2001. Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J. Exp. Med. 194:11–12
    [Google Scholar]
  123. 123. 
    Chang S-C, Momburg F, Bhutani N, Goldberg AL 2005. The ER aminopeptidase, ERAP1, trims precursors to lengths of MHC class I peptides by a “molecular ruler” mechanism. PNAS 102:4717107–12
    [Google Scholar]
  124. 124. 
    Saveanu L, Carroll O, Lindo V, Del Val M, Lopez D et al. 2005. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 6:7689–97
    [Google Scholar]
  125. 125. 
    Caron E, Kowalewski DJ, Chiek Koh C, Sturm T, Schuster H, Aebersold R 2015. Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol. Cell. Proteom. 14:123105–17
    [Google Scholar]
  126. 126. 
    Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W et al. 2017. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46:2315–26
    [Google Scholar]
  127. 127. 
    Prilliman K, Lindsey M, Zuo Y, Jackson KW, Zhang Y, Hildebrand W 1997. Large-scale production of class I bound peptides: assigning a signature to HLA-B*1501. Immunogenetics 45:6379–85
    [Google Scholar]
  128. 128. 
    Andreatta M, Lund O, Nielsen M 2013. Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach. Bioinformatics 29:18–14
    [Google Scholar]
  129. 129. 
    Bassani-Sternberg M, Pletscher-Frankild S, Jensen LJ, Mann M 2015. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell Proteom. 14:3658–73
    [Google Scholar]
  130. 130. 
    Nielsen M, Connelley T, Ternette N 2018. Improved prediction of bovine leucocyte antigens (BoLA) presented ligands by use of mass-spectrometry-determined ligand and in vitro binding data. J. Proteome Res. 17:1559–67
    [Google Scholar]
  131. 131. 
    Pearson H, Daouda T, Granados DP, Durette C, Bonneil E et al. 2016. MHC class I-associated peptides derive from selective regions of the human genome. J. Clin. Investig. 126:124690–701
    [Google Scholar]
  132. 132. 
    Bassani-Sternberg M, Chong C, Guillaume P, Solleder M, Pak H et al. 2017. Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. PLOS Comput. Biol. 13:8e1005725
    [Google Scholar]
  133. 133. 
    Barra C, Alvarez B, Paul S, Sette A, Peters B et al. 2018. Footprints of antigen processing boost MHC class II natural ligand predictions. Genome Med 10:184
    [Google Scholar]
  134. 134. 
    Kaufman J. 2018. Generalists and specialists: a new view of how MHC class I molecules fight infectious pathogens. Trends Immunol 39:5367–79
    [Google Scholar]
  135. 135. 
    Svitek N, Hansen AM, Steinaa L, Saya R, Awino E et al. 2014. Use of “one-pot, mix-and-read” peptide-MHC class I tetramers and predictive algorithms to improve detection of cytotoxic T lymphocyte responses in cattle. Vet. Res. 45:150
    [Google Scholar]
  136. 136. 
    Andreatta M, Jurtz VI, Kaever T, Sette A, Peters B, Nielsen M 2017. Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules. Immunology 152:2255–64
    [Google Scholar]
  137. 137. 
    Liepe J, Marino F, Sidney J, Jeko A, Bunting DE et al. 2016. A large fraction of HLA class I ligands are proteasome-generated spliced peptides. Science 354:6310354–58
    [Google Scholar]
  138. 138. 
    Faridi P, Li C, Ramarathinam SH, Vivian JP, Illing PT et al. 2018. A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands. Sci. Immunol. 3:28eaar3947
    [Google Scholar]
  139. 139. 
    Peters B, Brenner SE, Wang E, Slonim D, Kann MG 2018. Putting benchmarks in their rightful place: the heart of computational biology. PLOS Comput. Biol. 14:11e1006494
    [Google Scholar]
  140. 140. 
    Braendstrup P, Mortensen BK, Justesen S, Osterby T, Rasmussen M et al. 2014. Identification and HLA-tetramer-validation of human CD4+ and CD8+ T cell responses against HCMV proteins IE1 and IE2. PLOS ONE 9:4e94892
    [Google Scholar]
  141. 141. 
    Perez CL, Larsen MV, Gustafsson R, Norstrom MM, Atlas A et al. 2008. Broadly immunogenic HLA class I supertype-restricted elite CTL epitopes recognized in a diverse population infected with different HIV-1 subtypes. J. Immunol. 180:75092–100
    [Google Scholar]
  142. 142. 
    Weiskopf D, Angelo MA, de Azeredo EL, Sidney J, Greenbaum JA et al. 2013. Comprehensive analysis of dengue virus-specific responses supports an HLA-linked protective role for CD8+ T cells. PNAS 110:22E2046–53
    [Google Scholar]
  143. 143. 
    Lindestam Arlehamn CS, Sette A 2014. Definition of CD4 immunosignatures associated with MTB. Front. Immunol. 5:124
    [Google Scholar]
  144. 144. 
    Bjerregaard A-M, Nielsen M, Jurtz V, Barra CM, Hadrup SR et al. 2017. An analysis of natural T cell responses to predicted tumor neoepitopes. Front. Immunol. 8:1566
    [Google Scholar]
  145. 145. 
    Editorial 2017. The problem with neoantigen prediction. Nat. Biotechnol. 35:297
    [Google Scholar]
  146. 146. 
    Hoof I, van Baarle D, Hildebrand WH, Keşmir C 2012. Proteome sampling by the HLA class I antigen processing pathway. PLOS Comput. Biol. 8:5e1002517
    [Google Scholar]
  147. 147. 
    Juncker AS, Larsen MV, Weinhold N, Nielsen M, Brunak S, Lund O 2009. Systematic characterisation of cellular localisation and expression profiles of proteins containing MHC ligands. PLOS ONE 4:10e7448
    [Google Scholar]
  148. 148. 
    Yadav M, Jhunjhunwala S, Phung QT, Lupardus P, Tanguay J et al. 2014. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515:7528572–76
    [Google Scholar]
  149. 149. 
    Croft NP, Smith SA, Pickering J, Sidney J, Peters B et al. 2019. Most viral peptides displayed by class I MHC on infected cells are immunogenic. PNAS 116:83112–17
    [Google Scholar]
  150. 150. 
    Marino F, Chong C, Michaux J, Bassani-Sternberg M 2019. High-throughput, fast, and sensitive immunopeptidomics sample processing for mass spectrometry. Immune Checkpoint Blockade: Methods and Protocols Y Pico de Coaña 67–79 New York: Humana
    [Google Scholar]
  151. 151. 
    Andreatta M, Nicastri A, Peng X, Hancock G, Dorrell L et al. 2019. MS-rescue: a computational pipeline to increase the quality and yield of immunopeptidomics experiments. Proteomics 19:4e1800357
    [Google Scholar]
  152. 152. 
    Konda P, Murphy JP, Nielsen M, Gujar S 2019. Enhancing mass spectrometry-based MHC-I peptide identification through a targeted database search approach. Immunoproteomics: Methods and Protocols KM Fulton, SM Twine 301–7 New York: Humana
    [Google Scholar]
  153. 153. 
    Bassani-Sternberg M. 2018. Mass spectrometry based immunopeptidomics for the discovery of cancer neoantigens. Peptidomics: Methods and Strategies M Schrader, L Fricker 209–21 New York: Humana
    [Google Scholar]
  154. 154. 
    Trolle T, Nielsen M. 2014. NetTepi: an integrated method for the prediction of T cell epitopes. Immunogenetics 66:7–8449–56
    [Google Scholar]
  155. 155. 
    Dhanda SK, Karosiene E, Edwards L, Grifoni A, Paul S et al. 2018. Predicting HLA CD4 immunogenicity in human populations. Front. Immunol. 9:1369
    [Google Scholar]
  156. 156. 
    Frankild S, de Boer RJ, Lund O, Nielsen M, Kesmir C 2008. Amino acid similarity accounts for T cell cross-reactivity and for “holes” in the T cell repertoire. PLOS ONE 3:3e1831
    [Google Scholar]
  157. 157. 
    Bresciani A, Paul S, Schommer N, Dillon MB, Bancroft T et al. 2016. T-cell recognition is shaped by epitope sequence conservation in the host proteome and microbiome. Immunology 148:134–39
    [Google Scholar]
  158. 158. 
    Paul S, Sidney J, Sette A, Peters B 2016. TepiTool: a pipeline for computational prediction of T cell epitope candidates. Curr. Protoc. Immunol. 114:18.19.1–18.19.24
    [Google Scholar]
  159. 159. 
    Schubert B, Lund O, Nielsen M 2013. Evaluation of peptide selection approaches for epitope-based vaccine design. Tissue Antigens 82:4243–51
    [Google Scholar]
  160. 160. 
    Buus S. 1999. Description and prediction of peptide-MHC binding: the “human MHC project. .” Curr. Opin. Immunol. 11:2209–13
    [Google Scholar]
  161. 161. 
    Sidney J, Becart S, Zhou M, Duffy K, Lindvall M et al. 2017. Citrullination only infrequently impacts peptide binding to HLA class II MHC. PLOS ONE 12:5e0177140
    [Google Scholar]
  162. 162. 
    Zarling AL, Polefrone JM, Evans AM, Mikesh LM, Shabanowitz J et al. 2006. Identification of class I MHC-associated phosphopeptides as targets for cancer immunotherapy. PNAS 103:4014889–94
    [Google Scholar]
  163. 163. 
    Andersen MH, Bonfill JE, Neisig A, Arsequell G, Sondergaard I et al. 1999. Phosphorylated peptides can be transported by TAP molecules, presented by class I MHC molecules, and recognized by phosphopeptide-specific CTL. J. Immunol. 163:73812–18
    [Google Scholar]
  164. 164. 
    Klausen MS, Jespersen MC, Nielsen H, Jensen KK, Jurtz VI et al. 2019. NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning. Proteins 87:6520–27
    [Google Scholar]
  165. 165. 
    Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O et al. 2019. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37:4420–23
    [Google Scholar]
  166. 166. 
    Jurtz VI, Johansen AR, Nielsen M, Almagro Armenteros JJ, Nielsen H et al. 2017. An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics 33:223685–90
    [Google Scholar]
  167. 167. 
    Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ, Murphy T et al. 2018. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nat. Biotechnol. 37:56–63
    [Google Scholar]
  168. 168. 
    Cho S, Kang J. 2019. Dissociation kinetics of TAPBPR-MHC class I complex. Mol. Immunol. 114:661–62
    [Google Scholar]
  169. 169. 
    Dash P, Fiore-Gartland AJ, Hertz T, Wang GC, Sharma S et al. 2017. Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547:766189–93
    [Google Scholar]
  170. 170. 
    Glanville J, Huang H, Nau A, Hatton O, Wagar LE et al. 2017. Identifying specificity groups in the T cell receptor repertoire. Nature 547:766194–98
    [Google Scholar]
  171. 171. 
    Lanzarotti E, Marcatili P, Nielsen M 2019. T-cell receptor cognate target prediction based on paired α and β chain sequence and structural CDR loop similarities. Front. Immunol. 10:2080
    [Google Scholar]
  172. 172. 
    Lanzarotti E, Marcatili P, Nielsen M 2017. Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring. Mol. Immunol. 94:91–97
    [Google Scholar]
  173. 173. 
    Jurtz VI, Jessen LE, Bentzen AK, Jespersen MC, Mahajan S et al. 2018. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks. bioRxiv 433706. https://doi.org/10.1101/433706
    [Crossref]
  174. 174. 
    Zhang S-Q, Ma K-Y, Schonnesen AA, Zhang M, He C et al. 2018. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat. Biotechnol. 36:1156–59
    [Google Scholar]
  175. 175. 
    Flesch IEA, Woo W-P, Wang Y, Panchanathan V, Wong Y-C et al. 2010. Altered CD8+ T cell immu-nodominance after vaccinia virus infection and the naive repertoire in inbred and F1 mice. J. Immunol. 184:145–55
    [Google Scholar]
  176. 176. 
    Castelli FA, Szely N, Olivain A, Casartelli N, Grygar C et al. 2013. Hierarchy of CD4 T cell epitopes of the ANRS Lipo5 synthetic vaccine relies on the frequencies of pre-existing peptide-specific T cells in healthy donors. J. Immunol. 190:115757–63
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-021920-100259
Loading
/content/journals/10.1146/annurev-biodatasci-021920-100259
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error