Major histocompatibility complex linked databases and prediction tools for designing vaccines

Human Immunology

Volumn 77, Issue 3, 2016, Pages 295-306

  Singh, Satarudra Prakash ^a   Mishra, Bhartendu Nath ^b  

^a AMITY UNIVERSITY   (India)

^b INSTITUTE OF ENGINEERING AND TECHNOLOGY   (India)

Author keywords

Epitope; Immunoinformatics; MHC; Prediction; Supertype; Vaccine

Indexed keywords

BINDING PROTEIN; EPITOPE; MAJOR HISTOCOMPATIBILITY ANTIGEN; PROTEASOME; VACCINE; PEPTIDE; PROTEIN BINDING;

ALLELE; ANTIGEN BINDING; ANTIGEN PRESENTATION; ARTICLE; BIBLIOGRAPHIC DATABASE; DRUG DESIGN; MAJOR HISTOCOMPATIBILITY COMPLEX; MEDICAL INFORMATICS; PREDICTION; PRIORITY JOURNAL; T LYMPHOCYTE; BIOLOGY; EPITOPE MAPPING; GENETIC DATABASE; GENETICS; HUMAN; IMMUNOLOGY; METABOLISM; PROCEDURES; SOFTWARE; WEB BROWSER;

ALLELES; ANTIGEN PRESENTATION; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; EPITOPE MAPPING; EPITOPES, T-LYMPHOCYTE; HUMANS; MAJOR HISTOCOMPATIBILITY COMPLEX; PEPTIDES; PROTEASOME ENDOPEPTIDASE COMPLEX; PROTEIN BINDING; SOFTWARE; VACCINES; WEB BROWSER;

EID: 84947781482     PISSN: 01988859     EISSN: 18791166     Source Type: Journal    
DOI: 10.1016/j.humimm.2015.11.012     Document Type: Article

References (80)

1. Zhang G.L., Sun J., Chitkushev L., Brusic V. Big data analytics in immunology: a knowledge-based approach. Biomed. Res. Int. 2014, 2014:437987.
2. De Groot A.S. Immunomics: discovering new targets for vaccines and therapeutics. Drug Discov. Today 2006, 11:203.
3. He Y., Xiang Z., Mobley H.L. Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J. Biomed. Biotechnol. 2010, 2010:297505.
4. Heinson A.I., Woelk C.H., Newell M.L. The promise of reverse vaccinology. Int. Health. 2015, 7(2):85-89.
5. Hassainya Y., Garcia-Pons F., Kratzer R., Lindo V., Greer F., Lemonnier F.A., et al. Identification of naturally processed HLA-A2-restricted proinsulin epitopes by reverse immunology. Diabetes 2005, 54(7):2053-2059.
6. Zhao B.P., Chen L., Zhang Y.L., Yang J.M., Jia K., Sui C.Y., et al. In silico prediction of binding of promiscuous peptides to multiple MHC class-II molecules identifies the Th1 cell epitopes from secreted and transmembrane proteins of Schistosoma japonicum in BALB/c mice. Microbes Infect. 2011, 13(7):709-719.
7. Schwaiger J., Aberle J.H., Stiasny K., Knapp B., Schreiner W., Fae I., et al. Specificities of human CD4+ T cell responses to an inactivated flavivirus vaccine and infection: correlation with structure and epitope prediction. J. Virol. 2014, 88(14):7828-7842.
8. Fruci D., Lauvau G., Saveanu L., Amicosante M., Butler R.H., et al. Quantifying recruitment of cytosolic peptides for HLA class I presentation: impact of TAP transport. J. Immunol. 2003, 170(6):2977.
9. Sant'Angelo D.B., Robinson E., Janeway C.A., Denzin L.K. Recognition of core and flanking amino acids of MHC class II-bound peptides by the T-cell receptor. Eur. J. Immunol. 2002, 32:2510.
10. Rock K.L., Shen L. Cross-presentation: underlying mechanisms and role in immune surveillance. Immunol. Rev. 2005, 207:166-183.
11. Mizushima N. Autophagy: process and function. Genes Dev. 2007, 21(22):2861-2873.
12. Chemali M., Radtke K., Desjardins M., English L. Alternative pathways for MHC class I presentation: a new function for autophagy. Cell. Mol. Life Sci. 2011, 68(9):1533-1541.
13. Robinson J., Halliwell J.A., Hayhurst J.H., Flicek P., Parham P., Marsh S.G.E. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res. 2015, 43:D423-D431.
14. Vita R., Overton J.A., Greenbaum J.A., Ponomarenko J., Clark J.D., Cantrell J.R., et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 2015, 43:D405-D412.
15. Zhang G.L., DeLuca D.S., Keskin D.B., Chitkushev L., Zlateva T., Lund O., et al. MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles. J. Immunol. Methods 2011, 374(1-2):53-61.
16. Desai D.V., Kulkarni-Kale U. T-cell epitope prediction methods: an overview. Methods Mol. Biol. 2014, 1184:333-364.
17. Sidney J., Grey H.M., Southwood S., Celis E., Wentworth P.A., del Guercio M.F., et al. Definition of an HLA-A3-like supermotif demonstrates the overlapping peptide-binding repertoires of common HLA molecules. Hum. Immunol. 1996, 45(2):79-93.
18. Dzuris J.L., Sidney J., Appella E., Chesnut R.W., Watkins D.I., Sette A. Conserved MHC class I peptide binding motif between humans and rhesus macaques. J. Immunol. 2000, 164(1):283-291.
19. Lafuente E.M., Reche P.A. Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr. Pharm. Des. 2009, 15(28):3209-3220.
20. Singh S.P., Mishra B.N. Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network. Bioinformation 2008, 3(4):150-155.
21. Chowell D., Krishna S., Becker P.D., Cocita C., Shu J., Tan X., et al. TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+ T cell epitopes. Proc. Natl. Acad. Sci. U.S.A. 2015, 112(14):E1754-E1762.
22. Dönnes P. Support vector machine-based prediction of MHC-binding peptides. Methods Mol. Biol. 2007, 409:273-282.
23. Noguchi H., Kato R., Hanai T., Matsubara Y., Honda H., Brusic V., et al. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J. Biosci. Bioeng. 2002, 94(3):264-270.
24. Altuvia Y., Sette A., Sidney J., Southwood S., Margalit H. A structure-based algorithm to predict potential binding peptides to MHC molecules with hydrophobic binding pockets. Hum. Immunol. 1997, 58(1):1-11.
25. Schueler-Furman O., Altuvia Y., Sette A., Margalit H. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci. 2000, 9(9):1838-1846.
26. Kumar N., Mohanty D. MODPROPEP: a program for knowledge-based modeling of protein-peptide complexes. Nucleic Acids Res. 2007, 35:W549-W555.
27. Singh S.P., Mishra B.N. Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: implications for vaccine design. Bioinformation 2008, 3(2):72-82.
28. Tong J.C., Tan T.W., Ranganathan S. Modeling the structure of bound peptide ligands to major histocompatibility complex. Protein Sci. 2004, 13(9):2523-2532.
29. Bui H.H., Schiewe A.J., von Grafenstein H., Haworth I.S. Structural prediction of peptides binding to MHC class I molecules. Proteins 2006, 63(1):43-52.
30. Todman S.J., Halling-Brown M.D., Davies M.N., Flower D.R., Kayikci M., Moss D.S. Toward the atomistic simulation of T cell epitopes automated construction of MHC: peptide structures for free energy calculations. J. Mol. Graph. Model. 2008, 26(6):957-961.
31. Pierce B.G., Weng Z. A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes. Protein Sci. 2013, 22(1):35-46.
32. Bhasin M., Raghava G.P. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J. Biosci. 2007, 32(1):31-42.
33. Antes I., Siu S.W., Lengauer T. DynaPred: a structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations. Bioinformatics 2006, 22(14):e16-e24.
34. Bordner A.J. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. PLoS ONE 2010, 5(12):e14383.
35. Lin H.H., Ray S., Tongchusak S., Reinherz E.L., Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol. 2008, 9:8.
36. Paul S., Weiskopf D., Angelo M.A., Sidney J., Peters B., Sette A. HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J. Immunol. 2013, 191(12):5831-5839.
37. Trolle T., Metushi I.G., Greenbaum J.A., Kim Y., Sidney J., Lund O., et al. Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics 2015, (In press).
38. Wang S., Bai Z., Han J., Tian Y., Shang X., Wang L., Li J., Wu Y. Improving the prediction of HLA class I-binding peptides using a supertype-based method. J. Immunol. Methods 2014, 405:109-120.
39. Carrasco Pro S., Zimic M., Nielsen M. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment. Tissue Antigens 2014, 83(2):94-100.
40. Wang P., Sidney J., Dow C., Mothé B., Sette A., Peters B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput. Biol. 2008, 4(4):e1000048.
41. Kim Y., Sidney J., Buus S., Sette A., Nielsen M., Peters B. Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions. BMC Bioinformatics 2014, 15:241.
42. S.P. Singh, B.N. Mishra, Prediction model of MHC class-II binding peptide motifs using sequence weighting method for vaccine design, International Conference on Advances in Computing and Communications, 2012, pp. 234-237.
43. Shen W.J., Zhang S., Wong H.S. An effective and efficient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles. Proteome Sci. 2013, 11(Suppl. 1):S15.
44. Paul S., Lindestam Arlehamn C.S., Scriba T.J., Dillon M.B., Oseroff C., Hinz D., et al. Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. J. Immunol. Methods 2015, S0022-1759(15). 00098-8.
45. Zhang H., Lundegaard C., Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 2009, 25(1):83-89.
46. Altuvia Y., Margalit H. Sequence signals for generation of antigenic peptides by the proteasome: implications for proteasomal cleavage mechanism. J. Mol. Biol. 2000, 295:879.
47. Yang X., Yu X. An introduction to epitope prediction methods and software. Rev. Med. Virol. 2009, 19(2):77-96.
48. Saxová P., Buus S., Brunak S., Keşmir C. Predicting proteasomal cleavage sites: a comparison of available methods. Int. Immunol. 2003, 15(7):781-787.
49. Liu T., Liu W., Song Z., Jiao C., Zhu M., Wang X. Computational prediction of the specificities of proteasome interaction with antigen protein. Cell. Mol. Immunol. 2009, 6(2):135.
50. Lu Y.F., Sheng H., Zhang Y., Li Z.Y. Computational prediction of cleavage using proteasomal in vitro digestion and MHC I ligand data. J. Zhejiang Univ. Sci. B. 2013, 14(9):816-828.
51. Calis J.J., Reinink P., Keller C., Kloetzel P.M., Kesmir C. Role of peptide processing predictions in T cell epitope identification: contribution of different prediction programs. Immunogenetics 2015, 67(2):85-93.
52. Fruci D., Lauvau G., Saveanu L., Amicosante M., Butler R.H., Polack A., et al. Quantifying recruitment of cytosolic peptides for HLA class I presentation: impact of TAP transport. J. Immunol. 2003, 170(6):2977.
53. Doytchinova I.A., Guan P., Flower D.R. EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics 2006, 7:131.
54. Singh S.P., Khan F., Mishra B.N. Computational characterization of Plasmodium falciparum proteomic data for screening of potential vaccine candidates. Hum. Immunol. 2010, 71(2):136-143.
55. Singh R., Gupta P., Sharma P.K., Ades E.W., Hollingshead S.K., Singh S., et al. Prediction and characterization of helper T-cell epitopes from pneumococcal surface adhesin A. Immunology 2014, 141(4):514-530.
56. Singh S.P., Verma V., Mishra B.N. Characterization of Plasmodium falciparum proteome at asexual blood stages for screening of effective vaccine candidates: an immunoinformatics approach. Immunol. Immunogenet. Insights 2015, 7:21-30.
57. Iwai L.K., Yoshida M., Sidney J., Shikanai-Yasuda M.A., Goldberg A.C., Juliano M.A., et al. In silico prediction of peptides binding to multiple HLA-DR molecules accurately identifies immunodominant epitopes from gp43 of Paracoccidioides brasiliensis frequently recognized in primary peripheral blood mononuclear cell responses from sensitized individuals. Mol. Med. 2003, 9(9-12):209-219.
58. Mustafa A.S., Shaban F.A. ProPred analysis and experimental evaluation of promiscuous T-cell epitopes of three major secreted antigens of Mycobacterium tuberculosis. Tuberculosis 2006, 86:115.
59. Shi Y., Wu C., Zhou W.Y., Mao X.H., Guo G., Zou Q.M. Identification of H-2(d) restricted Th epitopes in Urease B subunit of Helicobacter pylori. Vaccine 2007, 25(14):2583.
60. Wen J.S., Jiang L.F., Zhou J.M., Yan H.J., Fang D.Y. Computational prediction and identification of dengue virus-specific CD4+ T-cell epitopes. Virus Res. 2008, 132:42.
61. Agallou M., Athanasiou E., Koutsoni O., Dotsika E., Karagouni E. Experimental validation of multi-epitope peptides including promising MHC class I- and II-restricted epitopes of four known Leishmania infantum proteins. Front. Immunol. 2014, 5:268.
62. Cannella A.P., Arlehamn C.S., Sidney J., Patra K.P., Torres K., Tsolis R.M., et al. Brucella melitensis T cell epitope recognition in humans with brucellosis in Peru. Infect. Immun. 2014, 82(1):124-131.
63. Oyarzun P., Ellis J.J., Gonzalez-Galarza F.F., Jones A.R., Middleton D., Boden M., et al. A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: application to emerging infectious diseases. Vaccine 2015, 33(10):1267-1273.
64. Moutaftsi M., Peters B., Pasquetto V., Tscharke D.C., Sidney J., Bui H.H., et al. A consensus epitope prediction approach identifies the breadth of murine T (CD8+)-cell responses to vaccinia virus. Nat. Biotechnol. 2006, 24:817.
65. Wang B., Yao K., Liu G., Xie F., Zhou F., Chen Y. Computational prediction and identification of Epstein-Barr virus latent membrane protein 2A antigen-specific CD8+ T-cell epitopes. Cell. Mol. Immunol. 2009, 6(2):97.
66. *0201-restricted cytotoxic T lymphocyte epitope from neutral endopeptidase. Immunol. Res. 2012, 52(3):231-239.
67. Li L., Yang B., Yu S., Zhang X., Lao S., Wu C. Human CD8+ T cells from TB pleurisy respond to four immunodominant epitopes in Mtb CFP10 restricted by HLA-B alleles. PLoS ONE 2013, 8(12):e82196.
68. *0201-binding epitope of HER-2/neu with potent antitumor properties. J. Immunol. 2008, 181(1):146.
69. Xing Q., Pang X.W., Peng J.R., Yin Y.H., Li Y., Yu X., et al. Identification of new cytotoxic T-lymphocyte epitopes from cancer testis antigen HCA587. Biochem. Biophys. Res. Commun. 2008, 372:331.
70. Hou Y., Guo Y., Wu C., Shen N., Jiang Y., Wang J. Prediction and identification of T cell epitopes in the H5N1 influenza virus nucleoprotein in chicken. PLoS ONE 2012, 7(6):e39344.
71. Lohia N., Baranwal M. Conserved peptides containing overlapping CD4+ and CD8+ T-cell epitopes in the H1N1 influenza virus: an immunoinformatics approach. Viral Immunol. 2014, 27(5):225-234.
72. Zhong W., Reche P.A., Lai C.C., Reinhold B., Reinherz E.L. Genome-wide characterization of a viral cytotoxic T lymphocyte epitope repertoire. J. Biol. Chem. 2003, 278(46):45135.
73. Drouin E.E., Glickstein L., Kwok W.W., Nepom G.T., Steere A.C. Searching for borrelial T cell epitopes associated with antibiotic-refractory Lyme arthritis. Mol. Immunol. 2008, 45:2323.
74. Nyárády Z., Czömpöly T., Bosze S., Nagy G., Petrohai A., Pál J., et al. Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies. Mol. Immunol. 2006, 43:830.
75. *0201. J. Immunol. 2004, 172:6783.
76. Heidema J., de Bree G.J., De Graaff P.M., van Maren W.W., Hoogerhout P., Out T.A., et al. Human CD8+ T cell responses against five newly identified respiratory syncytial virus-derived epitopes. J. Gen. Virol. 2004, 85:2365.
77. Crowe S.R., Miller S.C., Brown D.M., Adams P.S., Dutton R.W., Harmsen A.G., Lund F.E., Randall T.D., Swain S.L., Woodland D.L. Uneven distribution of MHC class II epitopes within the influenza virus. Vaccine 2006, 24:457.
78. Neumann F., Wagner C., Stevanovic S., Kubuschok B., Schormann C., Mischo A., et al. Identification of an HLA-DR-restricted peptide epitope with a promiscuous binding pattern derived from the cancer testis antigen HOM-MEL-40/SSX2. Int. J. Cancer 2004, 112:661.
79. Singh S.P., Roopendra K., Mishra B.N. Genome wide prediction of vaccine candidates for Leishmania major: An integrated approach. J Trop. Med. 2015, (in press).
80. Hsu S.C., Chang C.P., Tsai C.Y., Hsieh S.H., Wu-Hsieh B.A., Lo Y.S., et al. Steric recognition of T-cell receptor contact residues is required to map mutant epitopes by immunoinformatical programmes. Immunology 2012, 136(2):139-152.