Prediction of T-cell epitope

Journal of Pharmacological Sciences

Volumn 105, Issue 4, 2007, Pages 299-316

  Tsurui, Hiromichi ^a   Takahashi, Takuya ^b  

^a JUNTENDO UNIVERSITY SCHOOL OF MEDICINE   (Japan)

^b RITSUMEIKAN UNIVERSITY   (Japan)

Author keywords

Empirical scoring function; Matrix; MHC; Molecular dynamics (MD); Support vector machine (SVM)

Indexed keywords

AMINO ACID; EPITOPE; LIGAND; T LYMPHOCYTE ANTIGEN;

ACCURACY; ARTIFICIAL NEURAL NETWORK; ATOM; CALCULATION; COMPLEX FORMATION; FORCE; MAJOR HISTOCOMPATIBILITY COMPLEX; MOLECULAR DYNAMICS; PARAMETER; PERCEPTRON; PERFORMANCE; PREDICTION; PROTEIN BINDING; PROTEIN EXPRESSION; PROTEIN MOTIF; RECEPTOR AFFINITY; RECEPTOR BINDING; REVIEW; SCORING SYSTEM; SIMULATION; SUPPORT VECTOR MACHINE; T LYMPHOCYTE; THERMODYNAMICS;

EID: 37549028807     PISSN: 13478613     EISSN: 13478648     Source Type: Journal    
DOI: 10.1254/jphs.CR0070056     Document Type: Review

References (101)

1. Altman JD, Moss PA, Goulder PJ, Barouch DH, McHeyzer-Williams MG, Bell JI, et al. Phenotypic analysis of antigen-specific T lymphocytes. Science. 1996;274:94-96.
2. Rodriguez A, Regnault A, Kleijmeer M, Ricciardi-Castagnoli P, Amigorena S. Selective transport of internalized antigens to the cytosol for MHC class I presentation in dendritic cells. Nature Cell Biol. 1999;1:362-368.
3. Yewdell JW, Reits E, Neefjes J. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nat Rev Immunol. 2003;3:952-961.
4. Honey K, Rudensky AY. Lysosomal cysteine proteases regulate antigen presentation. Nat Rev Immunol. 2003;3:472-482.
5. Paz P, Brouwenstijn N, Perry R, Shastri N. Discrete proteolytic intermediates in the MHC class I antigen processing pathway and MHC independent peptide trimming in the ER. Mol Cell. 2003;12:565-576.
6. Norbury CC, Tewalt EF. Upstream toward the "DRiP"-ing source of the MHC class I pathway. Immunity. 2006;24:503-506.
7. Ashwell JD, Klusner RD. Genetic and mutational analysis of the T-cell antigen receptor. Annu Rev Immunol. 1990;8:139-167.
8. Wagner DH Jr. Re-shaping the T cell repertoire: TCR editing and TCR revision for good and for bad. Clin Immunol. 2007;123:1-6.
9. Kisielow P, Teh HS, Bluthmann H, von Boehmer H. Positive selection of antigen-specific T cells in thymus by restricting MHC molecules. Nature. 1988;335:730-733.
10. Bill J, Palmer E. Positive selection of CD4+ T cells mediated by MHC class II-bearing stromal cell in the thymic cortex. Nature. 1989;341:649-651.
11. Benoist C, Mathis D. Positive selection of the T cell repertoire: where and when does it occur? Cell. 1989;58:1027-1033.
12. von Boehmer H, Kisielow P. Self-nonself discrimination by T cells. Science. 1990;248:1369-1373.
13. Matsuzaki G, Yoshikai Y, Ogimoto M, Kishihara K, Nomoto K. Clonal deletion of self-reactive T cells at the early stage of T cell development in thymus of radiation bone marrow chimeras. J Immunol. 1990;145:46-51.
14. Gagnon SJ, Borbulevych OY, Davis-Harrison RL, Turner RV, Damirjian M, Wojnarowicz A, et al. T cell receptor recognition via cooperative conformational plasticity. J Mol Biol. 2006;363:228-243.
15. Teng MK, Smolyar A, Tse AG, Liu JH, Liu J, Hussey RE, et al. Identification of a common docking topology with substantial variation among different TCR-peptide-MHC complexes. Curr Biol. 1998;8:409-412.
16. Hennecke J, Carfi A, Wiley DC. 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. 2000;19:5611-5624.
17. Sant'Angelo DB, Waterbury G, Preston-Hurlburt P, Yoon ST, Medzhitov R, Hong SC, et al. The specificity and orientation of a TCR to its peptide-MHC class II ligands. Immunity. 1996;4:367-376.
18. Rammensee HG, Friede T, Stevanoviic S. MHC ligands and peptide motifs: first listing. Immunogenetics. 1995;41:178-228.
19. Jardetzky TS, Brown JH, Gorga JC, Stern LJ, Urban RG, Strominger JL, et al. Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II-like conformation for bound peptides. Proc Natl Acad Sci U S A. 1996;93:734-738.
20. Guex N, Peitsch MC. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis. 1997;18:2714-2723.
21. Kropshofer H, Max H, Muller CA, Hesse F, Stevanovic S, Jung G, et al. Self-peptide released from class II HLA-DR1 exhibits a hydrophobic two-residue contact motif. J Exp Med. 1992;175:1799-1803.
22. Hill CM, Hayball JD, Allison AA, Rothbard JB. Conformational and structural characteristics of peptides binding to HLA-DR molecules. J Immunol. 1991;147:189-197.
23. O'Sullivan D, Sidney J, Appella E, Walker L, Phillips L, Colon SM, et al. Characterization of the specificity of peptide binding to four DR haplotypes. J Immunol. 1990;145:1799-1808.
24. Rudensky A, Preston-Hurlburt P, Hong SC, Barlow A, Janeway CA Jr. Sequence analysis of peptides bound to MHC class II molecules. Nature. 1991;353:622-627.
25. Rudensky A, Preston-Hurlburt P, al-Ramadi BK, Rothbard J, Janeway CA Jr. Truncation variants of peptides isolated from MHC class II molecules suggest sequence motifs. Nature. 1992;359:429-431.
26. Cole GA, Tao T, Hogg TL, Ryan KW, Woodland DL. Binding motifs predict major histocompatibility complex class II-restricted epitopes in the Sendai virus M protein. J Virol. 1995;69:8057-8060.
27. Hammer J, Takacs B, Sinigaglia F. Identification of a motif for HLA-DR1 binding peptides using M13 display libraries. J Exp Med. 1992;176:1007-1013.
28. Hammer J, Valsasnini P, Tolba K, Bolin D, Higelin J, Takacs B, et al. Promiscuous and allele-specific anchors in HLA-DR-binding peptides. Cell. 1993;74:197-203.
29. Hammer J, Belunis C, Bolin D, Papadopoulos J, Walsky R, Higelin J, et al. High-affinity binding of short peptides to major histocompatibility complex class II molecules by anchor combinations. Proc Natl Acad Sci U S A. 1994;91:4456-4460.
30. Brusic V, Rudy G, Harrison LC. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 1998;26:368-371.
31. Stratmann T, Apostolopoulos V, Mallet-Designe V, Corper AL, Scott CA, Wilson IA, et al. The I-Ag7 MHC class II molecule linked to murine diabetes is a promiscuous peptide binder. J Immunol. 2000;165:3214-3225.
32. Maverakis E, van den Elzen P, Sercarz EE. Self-reactive T cells and degeneracy of T cell recognition: evolving concepts-from sequence homology to shape mimicry and TCR flexibility. J Autoimmun. 2001;16:201-209.
33. Anderton SM, Viner NJ, Matharu P, Lowrey PA, Wraith DC. Influence of a dominant cryptic epitope on autoimmune T cell tolerance. Nat Immunol. 2002;3:175-181.
34. He XL, Radu C, Sidney J, Sette A, Ward ES, Garcia KC. Structural snapshot of aberrant antigen presentation linked to autoimmunity: the immunodominant epitope of MBP complexed with I-Au. Immunity. 2002;17:83-94.
35. Seamons A, Sutton J, Bai D, Baird E, Bonn N, Kafsack BF, et al. Competition between two MHC binding registers in a single peptide processed from myelin basic protein influences tolerance and susceptibility to autoimmunity. J Exp Med. 2003;197:1391-1397.
36. Bankovich AJ, Girvin AT, Moesta AK, Garcia KC. Peptide register shifting within the MHC groove: theory becomes reality. Mol Immunol. 2004;40:1033-1039.
37. Hammer J, Bono E, Gallazzi F, Belunis C, Nagy Z, Sinigaglia F. Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J Exp Med. 1994;180:2353-2358.
38. Parker KC, Bednarek MA, Coligan JE. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol. 1994;152:163-175.
39. Kubo RT, Sette A, Grey HM, Appella E, Sakaguchi K, Zhu NZ, et al. Definition of specific peptide motifs for four major HLA-A alleles. J Immunol. 1994;152:3913-3924.
40. Davenport MP, Ho Shon IA, Hill AV. An empirical method for the prediction of T-cell epitopes. Immunogenetics. 1995;42:392-397.
41. Udaka K, Wiesmuller KH, Kienle S, Jung G, Walden P. Decrypting the structure of major histocompatibility complex class I-restricted cytotoxic T lymphocyte epitopes with complex peptide libraries. J Exp Med. 1995;181:2097-2108.
42. Udaka K, Wiesmuller KH, Kienle S, Jung G, Tamamura H, Yamagishi H, et al. An automated prediction of MHC class Ibinding peptides based on positional scanning with peptide libraries. Immunogenetics. 2000;51:816-828.
43. Brusic V, Schonbach C, Takiguchi M, Ciesielski V, Harrison LC. Application of genetic search in derivation of matrix models of peptide binding to MHC molecules. Proc Int Conf Intell Syst Mol Biol. 1997;5:75-83.
44. Schonbach C, Ibe M, Shiga H, Takamiya Y, Miwa K, Nokihara K, et al. Fine tuning of peptide binding to HLA-B*3501 molecules by nonanchor residues. J Immunol. 1995;154:5951-5958.
45. Schonbach C, Miwa K, Ibe M, Shiga H, Nokihara K, Takiguchi M. Refined peptide HLA-B*3501 binding motif reveals differences in 9-mer to 11-mer peptide binding. Immunogenetics. 1996;45:121-129.
46. Kondo A, Sidney J, Southwood S, del Guercio MF, Appella E, Sakamoto H, et al. Prominent roles of secondary anchor residues in peptide binding to HLA-A24 human class I molecules. J Immunol. 1995;155:4307-4312.
47. Rothbard JB, Marshall K, Wilson KJ, Fugger L, Zaller D. Prediction of peptide affinity to HLA DRB1*0401. Int Arch Allergy Immunol. 1994;105:1-7.
48. Zhao Y, Gran B, Pinilla C, Markovic-Plese S, Hemmer B, Tzou A, et al. Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic TCR and MHC peptide ligands. J Immunol. 2001;167:2130-2141.
49. Hemmer B, Vergelli M, Gran B, Ling N, Conlon P, Pinilla C, et al. Predictable TCR antigen recognition based on peptide scans leads to the identification of agonist ligands with no sequence homology. J Immunol. 1998;160:3631-3636.
50. Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol. 1999;17:555-561.
51. Vapnik VN. The nature of statistical learning theory. Springer. 2000.
52. Joachims T. Chapter 11. In: Schölkopf B, Burges C, Smola A, editors. Making large-scale SVM learning practical. Advances in Kernel methods - support vector learning. Cambridge, MA: MTT Press; 1999.
53. Dönnes P, Elofsson A. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics. 2002;3:25-32.
54. Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 1999;50:213-219.
55. Bhasin M, Raghava GP. SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics. 2004;20:421-423.
56. Kawashima S, Ogata H, Kanehisa M. AAindex: Amino Acid Index Database. Nucleic Acids Res. 1999;27:368-369.
57. Liu W, Meng X, Xu Q, Flower DR, Li T. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics. 2006;7:182.
58. Toseland CP, Clayton DJ, McSparron H, Hemsley SL, Blythe MJ, Paine K, et al. AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res. 2005;1:4.
59. Cui J, Han LY, Lin HH, Zhang HL, Tang ZQ, Zheng CJ, et al. Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol Immunol. 2007;44:866-877.
60. Kidera A, Konishi Y, Oka M, Ooi T, Scheraga HA. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. Protein J. 1985;4:23-55.
61. Zhao Y, Pinilla C, Valmori D, Martin R, Simon R. Application of support vector machines for T-cell epitopes prediction. Bioinformatics. 2003;19:1978-1984.
62. Rubio-Godoy V, Dutoit V, Zhao Y, Simon R, Guillaume P, Houghten R, et al. Positional scanning-synthetic peptide library-based analysis of self- and pathogen-derived peptide cross-reactivity with tumor-reactive Melan-A-specific CTL. J Immunol. 2002;169:5696-5707.
63. Takahashi T, Sugiura J, Nagayama K. Comparison of all atom, continuum, and linear fitting empirical models for charge screening effect of aqueous medium surrounding a protein molecule. J Chem Physics. 2002;116:8232.
64. Böhm HJ. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des. 1994;8:243-256.
65. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des. 1997;11:425-445.
66. Wang R, Lai L, Wang S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des. 2002;16:11-26.
67. Froloff N, Windemuth A, Honig B. On the calculation of binding free energies using continuum methods: application to MHC class I protein-peptide interactions. Protein Sci. 1997;6:1293-1301.
68. Böhm HJ. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des. 1998;12:309-323.
69. Rognan D, Lauemoller SL, Holm A, Buus S, Tschinke V. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J Med Chem. 1999;42:4650-4658.
70. Kasper P, Christen P, Gehring H. Empirical calculation of the relative free energies of peptide binding to the molecular chaperone DnaK. Proteins. 2000;40:185-192.
71. Logean A, Rognan D. Recovery of known T-cell epitopes by computational scanning of a viral genome. J Comput Aided Mol Des. 2002;16:229-243.
72. Hansson T, Marelius J, Aqvist J. Ligand binding affinity prediction by linear interaction energy methods. J Comput Aided Mol Des. 1998;12:27-35.
73. Srinivasan J, Cheatham TE, Cieplak P, Kollman PA, Case DA. Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate-DNA helices. J Am Chem Soc. 1998;120:9401-9409.
74. Massova I, Kollman PA. Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect Drug Discov Design. 2000;18:113-135.
75. Nicholls A, Honig B. A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J Comput Chem. 1991;12:435-445.
76. Baker N, Holst M, Wang F. Adaptive multilevel finite element solution of the Poisson-Boltzmann equation II. Refinement at solvent-accessible surfaces in biomolecular systems. J Computat Chem. 2000;21:1343-1352.
77. Lee B, Richards FM. The interpretation of protein structures: estimation of static accessibility. J Mol Biol. 1971;55:379-400.
78. Connolly ML. Solvent-accessible surfaces of proteins and nucleic acids. Science. 1983;221:709-713.
79. Chothia C. Hydrophobic bonding and accessible surface area in proteins. Nature. 1974;248:338-339.
80. Takahashi T, Nagayama K, Hogyoku M. Inter-subunit electrostatic interactions in ferritin molecule: Comparison with intermolecular interactions in crystals. J Crystal Growth. 1996;168:130-137.
81. Carlsson J, Ander M, Nervall M, Aqvist J. Continuum solvation models in the linear interaction energy method. J Phys Chem B. 2006;110:12034-12041.
82. Aqvist J, Luzhkov VB, Brandsdal BO. Ligand binding affinities from MD simulations. Acc Chem Res. 2002;35:358-365.
83. Almlof M, Aqvist J, Smalas AO, Brandsdal BO. Probing the effect of point mutations at protein-protein interfaces with free energy calculations. Biophys J. 2006;90:433-442.
84. Huo S, Massova I, Kollman PA. Computational alanine scanning of the 1:1 human growth hormone-receptor complex. J Computat Chem. 2002;23:15-27.
85. Massova I, Kollman P. Computational alanine scanning to probe protein-protein interactions: a novel application to evaluate binding free energies. J Am Chem Soc. 1999;121:8142-8143.
86. Li X, Zhang W, Qiao X, Xu X. Prediction of binding for a kind of non-peptic HCV NS3 serine protease inhibitors from plants by molecular docking and MM-PBSA method. Bioorg Med Chem. 2007;15:220-226.
87. Zoete V, Michielin O, Karplus M. Protein-ligand binding free energy estimation using molecular mechanics and continuum electrostatics. Application to HIV-1 protease inhibitors. J Comput Aided Mol Des. 2003;17:861-880.
88. Zoete V, Michielin O. Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex. Proteins. 2007;67:1026-1047.
89. Antes I, Siu SW, Lengauer T. DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations. Bioinformatics. 2006;22:e16-e24.
90. Yu K, Petrovsky N, Schonbach C, Koh JY, Brusic V. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med. 2002;8:137-148.
91. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res. 2000;33:889-897.
92. Pickett SD, Sternberg MJ. Empirical scale of side-chain conformational entropy in protein folding. J Mol Biol. 1993;231:825-839.
93. Wan S, Coveney PV, Flower DR. Peptide recognition by the T cell receptor: comparison of binding free energies from thermodynamic integration, Poisson-Boltzmann and linear interaction energy approximations. Philos Trans: Math Phys Eng Sci. 2005;363:2037-2053.
94. Kollman P. Free energy calculations: Applications to chemical and biochemical phenomena. Chem Rev. 1993;93:2395-2417.
95. Simonson T, Archontis G, Karplus M. Free energy simulations come of age: protein-ligand recognition. Acc Chem Res. 2002;35:430-437.
96. Wan S, Coveney PV, Flower DR. Molecular basis of peptide recognition by the TCR: affinity differences calculated using large scale computing. J Immunol. 2005;175:1715-1723.
97. Nakajima N, Nakamura H, Kidera A. Multicanonical ensemble generated by molecular dynamics simulation for enhanced conformational sampling of peptides. J Phys Chem B. 1997;101:817-824.
98. Shimizu H. Structural phase transition of Di-block polyampholyte. Mol Simulation. 1999;22:285-301.
99. Sugita Y, Okamoto Y. Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett. 1999;314:141-151.
100. Nguyen PH, Stock G, Mittag E, Hu CK, Li MS. Free energy landscape and folding mechanism of a beta-hairpin in explicit water: a replica exchange molecular dynamics study. Proteins. 2005;61:795-808.
101. Aqvist J, Medina C, Samuelsson JE. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994;7:385-391.