Bioinformatics tools for identifying T-cell epitopes

Drug Discovery Today: BIOSILICO

Volumn 2, Issue 1, 2004, Pages 18-23

  Brusic, Vladimir ^a   Flower, Darren R ^b  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

^b EDWARD JENNER INST FOR VACC RES   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EPITOPE;

ALLELE; ARTIFICIAL INTELLIGENCE; BINDING AFFINITY; BINDING SITE; BIOINFORMATICS; COMPUTER MODEL; COMPUTER PREDICTION; COMPUTER PROGRAM; DATA BASE; DRUG DESIGN; EPITOPE MAPPING; EVALUATION; INTERNET; MAJOR HISTOCOMPATIBILITY COMPLEX; MATHEMATICAL MODEL; MOLECULAR MODEL; PHENOTYPE; PROTEIN BINDING; PROTEIN MOTIF; REVIEW; T LYMPHOCYTE; VALIDATION PROCESS;

EID: 11344264868     PISSN: 17418364     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1741-8364(04)02374-1     Document Type: Review

References (42)

1. Dalton J.P., Mulcahy G. Parasite vaccines - a reality? Vet. Parasitol. 98:2001;149-167
2. Krebs S., Rognan D. From peptides to peptidomimetics: design of nonpeptide ligands for major histocompatibility proteins. Pharm. Acta Helv. 73:1998;173-181
3. Pichler W.J. Modes of presentation of chemical neoantigens to the immune system. Toxicology. 181-182:2002;49-54
4. Griffith J.P., et al. X-ray structure of calcineurin inhibited by the immunophilin-immunosuppressant FKBP12-FK506 complex. Cell. 82:1995;507-522
5. Tatsumi T., et al. MAGE-6 encodes HLA-DRbeta1*0401-presented epitopes recognized by CD4+ T cells from patients with melanoma or renal cell carcinoma. Clin. Cancer Res. 9:2003;947-954
6. Honeyman M.C., et al. Neural network-based prediction of candidate T-cell epitopes. Nat. Biotechnol. 16:1998;966-969
7. Brusic V., et al. Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. J. Mol. Graph. Model. 19:2001;405-411
8. De Lalla C., et al. Identification of novel T cell epitopes in Lol p5a by computational prediction. J. Immunol. 163:1999;1725-1729
9. Schönbach C., et al. Large-scale computational identification of HIV T-cell epitopes. Immunol. Cell Biol. 80:2002;300-306
10. De Groot A.S., et al. Rapid determination of HLA B*07 ligands from the West Nile virus NY99 genome. Emerg. Infect. Dis. 7:2001;706-713
11. De Groot A.S., et al. Immuno-informatics: mining genomes for vaccine components. Immunol. Cell Biol. 80:2002;255-269
12. Brusic V., Zeleznikow J. Computational binding assays of antigenic peptides. Lett. Pept. Sci. 6:1999;313-324
13. Berzofsky J.A., et al. Strategies for designing and optimizing new generation vaccines. Nat. Rev. Immunol. 1:2001;209-219
14. Robinson J., et al. IMGT/HLA and IMGT/MHC: sequence databases for the study of the major histocompatibility complex. Nucleic Acids Res. 31:2003;311-314
15. Madden D.R., et al. The antigenic identity of peptide-MHC complexes: a comparison of the conformations of five viral peptides presented by HLA-A2. Cell. 75:1993;693-708
16. Stern L.J., et al. Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide. Nature. 368:1994;215-221
17. Brusic V., et al. MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 26:1998;368-371
18. Falk K., et al. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature. 351:1991;290-296
19. Sette A., et al. Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc. Natl. Acad. Sci. U. S. A. 86:1989;3296-3300
20. Rammensee H.G., et al. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics. 50:1999;213-219
21. D'Amaro J., et al. A computer program for predicting possible cytotoxic T lymphocyte epitopes based on HLA class I peptide-binding motifs. Hum. Immunol. 43:1995;13-18
22. Stauss H.J., et al. Induction of cytotoxic T lymphocytes with peptides in vitro: identification of candidate T-cell epitopes in human papilloma virus. Proc. Natl. Acad. Sci. U. S. A. 89:1992;7871-7875
23. Kast W.M., et al. Role of HLA-A motifs in identification of potential CTL epitopes in human papillomavirus type 16 E6 and E7 proteins. J. Immunol. 152:1994;3904-3912
24. Parker K.C., et al. Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J. Immunol. 152:1994;163-175
25. Singh H., Raghava G.P. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics. 19:2003;1009-1014
26. Sturniolo T., et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol. 17:1999;555-561
27. Singh H., Raghava G.P. ProPred: prediction of HLA-DR binding sites. Bioinformatics. 17:2001;1236-1237
28. Udaka K., et al. An automated prediction of MHC class I-binding peptides based on positional scanning with peptide libraries. Immunogenetics. 51:2000;816-826
29. Brusic V., et al. Application of genetic search in derivation of matrix models of peptide binding to MHC molecules. Proc. Int. Conf. Intell. Syst. Mol. Biol. 5:1997;75-83
30. Borras-Cuesta F., et al. Specific and general HLA-DR binding motifs: comparison of algorithms. Hum. Immunol. 61:2000;266-278
31. Mamitsuka H. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins. 33:1998;460-474
32. Brusic V., et al. Prediction of promiscuous peptides that bind HLA class I molecules. Immunol. Cell Biol. 80:2002;280-285
33. Donnes P., Elofsson A. Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinform. 3:2002;25
34. Logean A., et al. Customized versus universal scoring functions: application to class I MHC-peptide binding free energy predictions. Bioorg. Med. Chem. Lett. 11:2001;675-679
35. Logean A., Rognan D. Recovery of known T-cell epitopes by computational scanning of a viral genome. J. Comput. Aided Mol. Des. 16:2002;229-243
36. Schueler-Furman O. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci. 9:2000;1838-1846
37. Doytchinova I.A., et al. Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201. J. Proteome Res. 1:2002;263-272
38. Doytchinova I.A., Flower D.R. A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J. Comput. Aided Mol. Des. 16:2002;535-544
39. Doytchinova I.A., Flower D.R. 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. 44:2001;3572-3581
40. Yu K., et al. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol. Med. 8:2002;137-148
41. Panigada M., et al. Identification of a promiscuous T-cell epitope in Mycobacterium tuberculosis Mce proteins. Infect. Immun. 70:2002;79-85
42. Zarour H.M., et al. NY-ESO-1 119-143 is a promiscuous major histocompatibility complex class II T-helper epitope recognized by Th1- and Th2-type tumor-reactive CD4+ T cells. Cancer Res. 62:2002;213-218