1932

Abstract

Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.

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2020-04-26
2024-04-18
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Literature Cited

  1. 1. 
    Paul WE. 2008. Fundamental Immunology Philadelphia, PA: Wolters Kluwer/Lippincott Williams & Wilkins
  2. 2. 
    Baumgartner JD, O'Brien TX, Kirkland TN, Glauser MP, Ziegler EJ 1987. Demonstration of cross-reactive antibodies to smooth gram-negative bacteria in antiserum to Escherichia coli J5. J. Infect. Dis. 156:136–43
    [Google Scholar]
  3. 3. 
    De Groot AS, Scott DW 2007. Immunogenicity of protein therapeutics. Trends Immunol 28:482–90
    [Google Scholar]
  4. 4. 
    Mazor R, King EM, Pastan I 2018. Strategies to reduce the immunogenicity of recombinant immunotoxins. Am. J. Pathol. 188:1736–43
    [Google Scholar]
  5. 5. 
    Sauna ZE, Lagasse D, Pedras-Vasconcelos J, Golding B, Rosenberg AS 2018. Evaluating and mitigating the immunogenicity of therapeutic proteins. Trends Biotechnol 36:1068–84
    [Google Scholar]
  6. 6. 
    Schumacher TN, Schreiber RD. 2015. Neoantigens in cancer immunotherapy. Science 348:69–74
    [Google Scholar]
  7. 7. 
    Madden DR. 1995. The three-dimensional structure of peptide-MHC complexes. Annu. Rev. Immunol. 13:587–622
    [Google Scholar]
  8. 8. 
    McDevitt HO. 2000. Discovering the role of the major histocompatibility complex in the immune response. Annu. Rev. Immunol. 18:1–17
    [Google Scholar]
  9. 9. 
    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 1–36
    [Google Scholar]
  10. 10. 
    Blum JS, Wearsch PA, Cresswell P 2013. Pathways of antigen processing. Annu. Rev. Immunol. 31:443–73
    [Google Scholar]
  11. 11. 
    Pamer E, Cresswell P. 1998. Mechanisms of MHC class I–restricted antigen processing. Annu. Rev. Immunol. 16:323–58
    [Google Scholar]
  12. 12. 
    Rammensee HG, Falk K, Rotzschke O 1993. Peptides naturally presented by MHC class I molecules. Annu. Rev. Immunol. 11:213–44
    [Google Scholar]
  13. 13. 
    Rock KL, Goldberg AL. 1999. Degradation of cell proteins and the generation of MHC class I-presented peptides. Annu. Rev. Immunol. 17:739–79
    [Google Scholar]
  14. 14. 
    Townsend A, Bodmer H. 1989. Antigen recognition by class I-restricted T lymphocytes. Annu. Rev. Immunol. 7:601–24
    [Google Scholar]
  15. 15. 
    Garcia KC, Teyton L, Wilson IA 1999. Structural basis of T cell recognition. Annu. Rev. Immunol. 17:369–97
    [Google Scholar]
  16. 16. 
    Rossjohn J, Gras S, Miles JJ, Turner SJ, Godfrey DI, McCluskey J 2015. T cell antigen receptor recognition of antigen-presenting molecules. Annu. Rev. Immunol. 33:169–200
    [Google Scholar]
  17. 17. 
    Rudolph MG, Stanfield RL, Wilson IA 2006. How TCRs bind MHCs, peptides, and coreceptors. Annu. Rev. Immunol. 24:419–66
    [Google Scholar]
  18. 18. 
    Unanue ER, Turk V, Neefjes J 2016. Variations in MHC class II antigen processing and presentation in health and disease. Annu. Rev. Immunol. 34:265–97
    [Google Scholar]
  19. 19. 
    Yewdell JW, Bennink JR. 1999. Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu. Rev. Immunol. 17:51–88
    [Google Scholar]
  20. 20. 
    Marrack P, Scott-Browne JP, Dai S, Gapin L, Kappler JW 2008. Evolutionarily conserved amino acids that control TCR-MHC interaction. Annu. Rev. Immunol. 26:171–203
    [Google Scholar]
  21. 21. 
    Shastri N, Schwab S, Serwold T 2002. Producing nature's gene-chips: the generation of peptides for display by MHC class I molecules. Annu. Rev. Immunol. 20:463–93
    [Google Scholar]
  22. 22. 
    Nobel Found 2019. The Nobel Prize in Physiology or Medicine 1980. The Nobel Prize https://www.nobelprize.org/prizes/medicine/1980/summary/
    [Google Scholar]
  23. 23. 
    Nobel Found 2019. The Nobel Prize in Physiology or Medicine 1996. The Nobel Prize https://www.nobelprize.org/prizes/medicine/1996/summary/
    [Google Scholar]
  24. 24. 
    Matzinger P. 1981. A one-receptor view of T-cell behaviour. Nature 292:497–501
    [Google Scholar]
  25. 25. 
    Berzofsky JA. 1980. Immune response genes in the regulation of mammalian immunity.. Biological Regulation and Development, Vol 2 RF Goldberger 467–94 Boston, MA: Springer
    [Google Scholar]
  26. 26. 
    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. 133:2067–74
    [Google Scholar]
  27. 27. 
    Shimonkevitz R, Kappler J, Marrack P, Grey H 1983. Antigen recognition by H-2-restricted T cells. I. Cell-free antigen processing. J. Exp. Med. 158:303–16
    [Google Scholar]
  28. 28. 
    Townsend AR, Gotch FM, Davey J 1985. Cytotoxic T cells recognize fragments of the influenza nucleoprotein. Cell 42:457–67
    [Google Scholar]
  29. 29. 
    Babbitt BP, Allen PM, Matsueda G, Haber E, Unanue ER 1985. Binding of immunogenic peptides to Ia histocompatibility molecules. Nature 317:359–61
    [Google Scholar]
  30. 30. 
    Buus S, Colon S, Smith C, Freed JH, Miles C, Grey HM 1986. Interaction between a “processed” ovalbumin peptide and Ia molecules. PNAS 83:3968–71
    [Google Scholar]
  31. 31. 
    DeLisi C, Berzofsky JA. 1985. T-cell antigenic sites tend to be amphipathic structures. PNAS 82:7048–52
    [Google Scholar]
  32. 32. 
    Rothbard JB, Townsend A, Edwards M, Taylor W 1987. Pattern recognition among T-cell epitopes. Haematol. Blood Transfus. 31:324–31
    [Google Scholar]
  33. 33. 
    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 235:1353–58
    [Google Scholar]
  34. 34. 
    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 328:395–99
    [Google Scholar]
  35. 35. 
    Benoist CO, Mathis DJ, Kanter MR, Williams VE 2nd, McDevitt HO 1983. Regions of allelic hypervariability in the murine A alpha immune response gene. Cell 34:169–77
    [Google Scholar]
  36. 36. 
    Bjorkman PJ, Saper MA, Samraoui B, Bennett WS, Strominger JL, Wiley DC 1987. Structure of the human class I histocompatibility antigen, HLA-A2. Nature 329:506–12
    [Google Scholar]
  37. 37. 
    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 364:33–39
    [Google Scholar]
  38. 38. 
    Pamer EG, Harty JT, Bevan MJ 1991. Precise prediction of a dominant class I MHC-restricted epitope of Listeriamonocytogenes. . Nature 353:852–55
    [Google Scholar]
  39. 39. 
    Rotzschke O, Falk K, Deres K, Schild H, Norda M et al. 1990. Isolation and analysis of naturally processed viral peptides as recognized by cytotoxic T cells. Nature 348:252–54
    [Google Scholar]
  40. 40. 
    Rotzschke O, Falk K, Wallny HJ, Faath S, Rammensee HG 1990. Characterization of naturally occurring minor histocompatibility peptides including H-4 and H-Y. Science 249:283–87
    [Google Scholar]
  41. 41. 
    Van Bleek GM, Nathenson SG 1990. Isolation of an endogenously processed immunodominant viral peptide from the class I H-2Kb molecule. Nature 348:213–16
    [Google Scholar]
  42. 42. 
    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 351:290–96
    [Google Scholar]
  43. 43. 
    Henderson RA, Cox AL, Sakaguchi K, Appella E, Shabanowitz J et al. 1993. Direct identification of an endogenous peptide recognized by multiple HLA-A2.1-specific cytotoxic T cells. PNAS 90:10275–79
    [Google Scholar]
  44. 44. 
    Hunt DF, Michel H, Dickinson TA, Shabanowitz J, Cox AL et al. 1992. Peptides presented to the immune system by the murine class II major histocompatibility complex molecule I-Ad. Science 256:1817–20
    [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 86:3296–300
    [Google Scholar]
  46. 46. 
    Ruppert J, Sidney J, Celis E, Kubo RT, Grey HM, Sette A 1993. Prominent role of secondary anchor residues in peptide binding to HLA-A2.1 molecules. Cell 74:929–37
    [Google Scholar]
  47. 47. 
    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]
  48. 48. 
    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 57:304–14
    [Google Scholar]
  49. 49. 
    Kotsiantis SB. 2007. Supervised machine learning: a review of classification techniques. Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies I Maglogiannis, K Karpouzis, M Wallace, J Soldatos 3–24 Amsterdam: IOS
    [Google Scholar]
  50. 50. 
    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. 152:163–75
    [Google Scholar]
  51. 51. 
    Adams HP, Koziol JA. 1995. Prediction of binding to MHC class I molecules. J. Immunol. Methods 185:181–90
    [Google Scholar]
  52. 52. 
    Brusic V, Rudy G, Harrison LC 1994. Prediction of MHC binding peptides using artificial neural networks. Complex Systems: Mechanism of Adaptation RJ Stonier, XH Yu, pp 253–60 Amsterdam: IOS
    [Google Scholar]
  53. 53. 
    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. 16:753–56
    [Google Scholar]
  54. 54. 
    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 12:1007–17
    [Google Scholar]
  55. 55. 
    Mamitsuka H. 1998. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:460–74
    [Google Scholar]
  56. 56. 
    Zhang C, Bickis MG, Wu FX, Kusalik AJ 2006. Optimally-connected hidden Markov models for predicting MHC-binding peptides. J. Bioinform. Comput. Biol. 4:959–80
    [Google Scholar]
  57. 57. 
    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. 44:3572–81
    [Google Scholar]
  58. 58. 
    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 19:1765–72
    [Google Scholar]
  59. 59. 
    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:e9272
    [Google Scholar]
  60. 60. 
    Grant BJ, Gorfe AA, McCammon JA 2010. Large conformational changes in proteins: signaling and other functions. Curr. Opin. Struct. Biol. 20:142–47
    [Google Scholar]
  61. 61. 
    Korber BTM, Moore JP, Brander C, Walker BD, Haynes BF, Koup R 1998. HIV Molecular Immunology Compendium Los Alamos, NM: Los Alamos Natl. Lab. Theor. Biol. Biophys.
  62. 62. 
    Bhasin M, Singh H, Raghava GP 2003. MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:665–66
    [Google Scholar]
  63. 63. 
    Peters B, Sidney J, Bourne P, Bui HH, Buus S et al. 2005. The immune epitope database and analysis resource: from vision to blueprint. PLOS Biol 3:e91
    [Google Scholar]
  64. 64. 
    Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S et al. 2019. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res 47:D339–43
    [Google Scholar]
  65. 65. 
    Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H et al. 2019. IEDB-AR: immune epitope database—analysis resource in 2019. Nucleic Acids Res 47:W502–6
    [Google Scholar]
  66. 66. 
    Shao W, Pedrioli PGA, Wolski W, Scurtescu C, Schmid E et al. 2018. The SysteMHC Atlas project. Nucleic Acids Res 46:D1237–47
    [Google Scholar]
  67. 67. 
    Lill JR, van Veelen PA, Tenzer S, Admon A, Caron E et al. 2018. Minimal Information About an Immuno-Peptidomics Experiment (MIAIPE). Proteomics 18:e1800110
    [Google Scholar]
  68. 68. 
    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:e65
    [Google Scholar]
  69. 69. 
    Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M 2008. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11. Nucleic Acids Res 36:W509–12
    [Google Scholar]
  70. 70. 
    Kim Y, Sidney J, Pinilla C, Sette A, Peters B 2009. Derivation of an amino acid similarity matrix for peptide: MHC binding and its application as a Bayesian prior. BMC Bioinform 10:394
    [Google Scholar]
  71. 71. 
    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. 94:264–70
    [Google Scholar]
  72. 72. 
    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. 44:866–77
    [Google Scholar]
  73. 73. 
    Salomon J, Flower DR. 2006. Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinform 7:501
    [Google Scholar]
  74. 74. 
    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 20:1388–97
    [Google Scholar]
  75. 75. 
    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 14:121–30
    [Google Scholar]
  76. 76. 
    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]
  77. 77. 
    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]
  78. 78. 
    Wang P, Sidney J, Dow C, Mothe B, Sette A, Peters B 2008. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLOS Comput. Biol. 4:e1000048
    [Google Scholar]
  79. 79. 
    Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J et al. 2006. A consensus epitope prediction approach identifies the breadth of murine TCD8+-cell responses to vaccinia virus. Nat. Biotechnol. 24:817–19
    [Google Scholar]
  80. 80. 
    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 154:394–406
    [Google Scholar]
  81. 81. 
    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 65:711–24
    [Google Scholar]
  82. 82. 
    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]
  83. 83. 
    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 152:255–64
    [Google Scholar]
  84. 84. 
    Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M 2015. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics 67:641–50
    [Google Scholar]
  85. 85. 
    Andreatta M, Nielsen M. 2016. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32:511–17
    [Google Scholar]
  86. 86. 
    Andreatta M, Schafer-Nielsen C, Lund O, Buus S, Nielsen M 2011. NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data. PLOS ONE 6:e26781
    [Google Scholar]
  87. 87. 
    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 8:33
    [Google Scholar]
  88. 88. 
    Nielsen M, Andreatta M. 2017. NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions. Nucleic Acids Res 45:W344–49
    [Google Scholar]
  89. 89. 
    Kim Y, Sidney J, Buus S, Sette A, Nielsen M, Peters B 2014. Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions. BMC Bioinform 15:241
    [Google Scholar]
  90. 90. 
    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]
  91. 91. 
    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. 17:555–61
    [Google Scholar]
  92. 92. 
    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:e796
    [Google Scholar]
  93. 93. 
    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]
  94. 94. 
    Jojic N, Reyes-Gomez M, Heckerman D, Kadie C, Schueler-Furman O 2006. Learning MHC I–peptide binding. Bioinformatics 22:e227–35
    [Google Scholar]
  95. 95. 
    Jacob L, Vert JP. 2008. Efficient peptide-MHC-I binding prediction for alleles with few known binders. Bioinformatics 24:358–66
    [Google Scholar]
  96. 96. 
    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 25:1293–99
    [Google Scholar]
  97. 97. 
    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 13:350–64
    [Google Scholar]
  98. 98. 
    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]
  99. 99. 
    Pfeifer N, Kohlbacher O. 2008. Multiple instance learning allows MHC class II epitope predictions across alleles. Algorithms in Bioinformatics KA Crandall, J Lagergren 210–21 Berlin, Heidelberg: Springer
    [Google Scholar]
  100. 100. 
    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:e1000107
    [Google Scholar]
  101. 101. 
    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. 182:1865–70
    [Google Scholar]
  102. 102. 
    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 57:33–41
    [Google Scholar]
  103. 103. 
    Bhasin M, Raghava GP. 2004. Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13:596–607
    [Google Scholar]
  104. 104. 
    Peters B, Bulik S, Tampe R, Van Endert PM, Holzhutter HG 2003. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J. Immunol. 171:1741–49
    [Google Scholar]
  105. 105. 
    Doytchinova IA, Guan P, Flower DR 2006. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinform 7:131
    [Google Scholar]
  106. 106. 
    Hakenberg J, Nussbaum AK, Schild H, Rammensee HG, Kuttler C et al. 2003. MAPPP: MHC class I antigenic peptide processing prediction. Appl. Bioinform. 2:155–58
    [Google Scholar]
  107. 107. 
    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. 35:2295–303
    [Google Scholar]
  108. 108. 
    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. 62:1025–37
    [Google Scholar]
  109. 109. 
    Stranzl T, Larsen MV, Lundegaard C, Nielsen M 2010. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62:357–68
    [Google Scholar]
  110. 110. 
    Gfeller D, Guillaume P, Michaux J, Pak HS, Daniel RT et al. 2018. The length distribution and multiple specificity of naturally presented HLA-I ligands. J. Immunol. 201:3705–16
    [Google Scholar]
  111. 111. 
    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. 196:1480–87
    [Google Scholar]
  112. 112. 
    Chang SC, 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 102:17107–12
    [Google Scholar]
  113. 113. 
    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. 6:689–97
    [Google Scholar]
  114. 114. 
    Wenzel T, Eckerskorn C, Lottspeich F, Baumeister W 1994. Existence of a molecular ruler in proteasomes suggested by analysis of degradation products. FEBS Lett 349:205–9
    [Google Scholar]
  115. 115. 
    Neefjes J, Jongsma ML, Paul P, Bakke O 2011. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat. Rev. Immunol. 11:823–36
    [Google Scholar]
  116. 116. 
    Sant AJ, Chaves FA, Leddon SA, Tung J 2013. The control of the specificity of CD4 T cell responses: thresholds, breakpoints, and ceilings. Front. Immunol. 4:340
    [Google Scholar]
  117. 117. 
    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 10:84
    [Google Scholar]
  118. 118. 
    Paul S, Karosiene E, Dhanda SK, Jurtz V, Edwards L et al. 2018. Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands. Front. Immunol. 9:1795
    [Google Scholar]
  119. 119. 
    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 7:387–99
    [Google Scholar]
  120. 120. 
    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 46:315–26
    [Google Scholar]
  121. 121. 
    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 45:379–85
    [Google Scholar]
  122. 122. 
    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. 14:658–73
    [Google Scholar]
  123. 123. 
    Andreatta M, Lund O, Nielsen M 2013. Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach. Bioinformatics 29:8–14
    [Google Scholar]
  124. 124. 
    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:e1005725
    [Google Scholar]
  125. 125. 
    Murphy JP, Konda P, Kowalewski DJ, Schuster H, Clements D et al. 2017. MHC-I ligand discovery using targeted database searches of mass spectrometry data: implications for T-cell immunotherapies. J. Proteome Res. 16:1806–16
    [Google Scholar]
  126. 126. 
    Bassani-Sternberg M, Gfeller D. 2016. Unsupervised HLA peptidome deconvolution improves ligand prediction accuracy and predicts cooperative effects in peptide-HLA interactions. J. Immunol. 197:2492–99
    [Google Scholar]
  127. 127. 
    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. 199:3360–68
    [Google Scholar]
  128. 128. 
    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 7:129–32.e4
    [Google Scholar]
  129. 129. 
    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. bioRxiv 550673. https://doi.org/10.1101/550673
    [Crossref]
  130. 130. 
    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. 153:5586–92
    [Google Scholar]
  131. 131. 
    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. 191:5831–39
    [Google Scholar]
  132. 132. 
    Bjerregaard AM, 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]
  133. 133. 
    Kosaloglu-Yalcin Z, Lanka M, Frentzen A, Logandha Ramamoorthy Premlal A, Sidney J et al. 2018. Predicting T cell recognition of MHC class I restricted neoepitopes. Oncoimmunology 7:e1492508
    [Google Scholar]
  134. 134. 
    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]
  135. 135. 
    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. 160:3363–73
    [Google Scholar]
  136. 136. 
    Sidney J, Peters B, Frahm N, Brander C, Sette A 2008. HLA class I supertypes: a revised and updated classification. BMC Immunol 9:1
    [Google Scholar]
  137. 137. 
    Greenbaum J, Sidney J, Chung J, Brander C, Peters B, Sette A 2011. Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes. Immunogenetics 63:325–35
    [Google Scholar]
  138. 138. 
    McKinney DM, Southwood S, Hinz D, Oseroff C, Arlehamn CS et al. 2013. A strategy to determine HLA class II restriction broadly covering the DR, DP, and DQ allelic variants most commonly expressed in the general population. Immunogenetics 65:357–70
    [Google Scholar]
  139. 139. 
    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:E2046–53
    [Google Scholar]
  140. 140. 
    Oseroff C, Sidney J, Kotturi MF, Kolla R, Alam R et al. 2010. Molecular determinants of T cell epitope recognition to the common Timothy grass allergen. J. Immunol. 185:943–55
    [Google Scholar]
  141. 141. 
    Paul S, Lindestam Arlehamn CS, Scriba TJ, Dillon MB, Oseroff C et al. 2015. Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. J. Immunol. Methods 422:28–34
    [Google Scholar]
  142. 142. 
    Marty Pyke R, Thompson WK, Salem RM, Font-Burgada J, Zanetti M, Carter H 2018. Evolutionary pressure against MHC class II binding cancer mutations. Cell 175:416–28.e13 Erratum. 2018. Cell 175(7):1991
    [Google Scholar]
  143. 143. 
    Marty R, Kaabinejadian S, Rossell D, Slifker MJ, van de Haar J et al. 2017. MHC-I genotype restricts the oncogenic mutational landscape. Cell 171:1272–83.e15
    [Google Scholar]
  144. 144. 
    Calis JJ, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD et al. 2013. Properties of MHC class I presented peptides that enhance immunogenicity. PLOS Comput. Biol. 9:e1003266
    [Google Scholar]
  145. 145. 
    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]
  146. 146. 
    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:eaar3947
    [Google Scholar]
  147. 147. 
    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 354:354–58
    [Google Scholar]
  148. 148. 
    Collins EJ, Garboczi DN, Wiley DC 1994. Three-dimensional structure of a peptide extending from one end of a class I MHC binding site. Nature 371:626–29
    [Google Scholar]
  149. 149. 
    Li X, Lamothe PA, Walker BD, Wang JH 2017. Crystal structure of HLA-B*5801 with a TW10 HIV Gag epitope reveals a novel mode of peptide presentation. Cell Mol. Immunol. 14:631–34
    [Google Scholar]
  150. 150. 
    McMurtrey C, Trolle T, Sansom T, Remesh SG, Kaever T et al. 2016. Toxoplasmagondii peptide ligands open the gate of the HLA class I binding groove. eLife 5:e12556
    [Google Scholar]
  151. 151. 
    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. 24:387–94
    [Google Scholar]
  152. 152. 
    Remesh SG, Andreatta M, Ying G, Kaever T, Nielsen M et al. 2017. Unconventional peptide presentation by major histocompatibility complex (MHC) class I allele HLA-A*02:01: BREAKING CONFINEMENT. J. Biol. Chem. 292:5262–70
    [Google Scholar]
  153. 153. 
    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 115:5083–88
    [Google Scholar]
  154. 154. 
    Greenbaum JA, Kotturi MF, Kim Y, Oseroff C, Vaughan K et al. 2009. Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population. PNAS 106:20365–70
    [Google Scholar]
  155. 155. 
    Sridhar S, Begom S, Bermingham A, Hoschler K, Adamson W et al. 2013. Cellular immune correlates of protection against symptomatic pandemic influenza. Nat. Med. 19:1305–12
    [Google Scholar]
  156. 156. 
    Westernberg L, Schulten V, Greenbaum JA, Natali S, Tripple V et al. 2016. T-cell epitope conservation across allergen species is a major determinant of immunogenicity. J. Allergy Clin. Immunol. 138:571–78.e7
    [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 148:34–39
    [Google Scholar]
  158. 158. 
    Klein L, Kyewski B, Allen PM, Hogquist KA 2014. Positive and negative selection of the T cell repertoire: what thymocytes see (and don't see). Nat. Rev. Immunol. 14:377–91
    [Google Scholar]
  159. 159. 
    Carrasco Pro S, Lindestam Arlehamn CS, Dhanda SK, Carpenter C, Lindvall M et al. 2018. Microbiota epitope similarity either dampens or enhances the immunogenicity of disease-associated antigenic epitopes. PLOS ONE 13:e0196551
    [Google Scholar]
  160. 160. 
    Moise L, Beseme S, Tassone R, Liu R, Kibria F et al. 2016. T cell epitope redundancy: cross-conservation of the TCR face between pathogens and self and its implications for vaccines and autoimmunity. Expert Rev. Vaccines 15:607–17
    [Google Scholar]
  161. 161. 
    Bui HH, Sidney J, Li W, Fusseder N, Sette A 2007. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinform 8:361
    [Google Scholar]
  162. 162. 
    Dhanda SK, Vaughan K, Schulten V, Grifoni A, Weiskopf D et al. 2018. Development of a novel clustering tool for linear peptide sequences. Immunology 155:331–45
    [Google Scholar]
  163. 163. 
    Chaves FA, Lee AH, Nayak JL, Richards KA, Sant AJ 2012. The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection. J. Immunol. 188:4235–48
    [Google Scholar]
  164. 164. 
    Mahajan S, Vita R, Shackelford D, Lane J, Schulten V et al. 2018. Epitope specific antibodies and T cell receptors in the immune epitope database. Front. Immunol. 9:2688
    [Google Scholar]
  165. 165. 
    Glanville J, Huang H, Nau A, Hatton O, Wagar LE et al. 2017. Identifying specificity groups in the T cell receptor repertoire. Nature 547:94–98
    [Google Scholar]
  166. 166. 
    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]
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