Neural network-based prediction of candidate T-cell epitopes

Nature Biotechnology

Volumn 16, Issue 10, 1998, Pages 966-969

  Honeyman, Margo C ^a   Brusic, Vladimir ^a   Stone, Natalie L ^a   Harrison, Leonard C ^a  

^a ROYAL MELBOURNE HOSPITAL   (Australia)

Author keywords

Applied immunology; Bioinformatics; MHC

Indexed keywords

EPITOPE; MAJOR HISTOCOMPATIBILITY ANTIGEN; PHOSPHATASE;

ANTIGEN PRESENTING CELL; ARTICLE; CELL PROLIFERATION; HLA SYSTEM; INSULIN DEPENDENT DIABETES MELLITUS; MAJOR HISTOCOMPATIBILITY COMPLEX; NERVE CELL NETWORK; PREDICTION; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; T LYMPHOCYTE ACTIVATION; X RAY CRYSTALLOGRAPHY;

AMINO ACID SEQUENCE; EPITOPES; HISTOCOMPATIBILITY ANTIGENS CLASS I; HOMOZYGOTE; HUMANS; MOLECULAR SEQUENCE DATA; NEURAL NETWORKS (COMPUTER); SEQUENCE HOMOLOGY, AMINO ACID; T-LYMPHOCYTES;

EID: 0031760687     PISSN: 10870156     EISSN: None     Source Type: Journal    
DOI: 10.1038/nbt1098-966     Document Type: Article

References (27)

1. DeLisi, C. and Berzofsky, J.A. 1985. T-cell antigenic sites tend to be amphipathic structures. Proc. Natl. Acad. Sci. USA 82:7048-7052.
2. Rothbard, J.B. and Taylor, W.R. 1988. A sequence pattern common to T-cell epitopes. EMBO J. 7:93-100.
3. Hammer, J., Sturnlolo, T., and Sinigaglia, R 1997. HLA class II peptide binding specificity and autoimmunity. Adv. Immunol. 66:67-100.
4. Rammensee, H-G., Friede, T., and Stevanovic, S. 1995. MHC ligands and peptide motifs: first listing. Immunogenetics 4:178-228.
5. Raddrizzani, L., Sturniolo, I., Guenot, J., Bono, E., Gallazzi, F., Nagy, Z.A. et al. 1997. Different modes of peptide interaction enable HLA-DQ and HLA-DR molecules to bind diverse peptide repertoires. J. Immunol. 159:703-711.
6. Jardetzky, T.S., Brown, J.H., Gorge J.C., Stern, L.J., Strominger, J.L., and Wiley, D.O. 1996. Crystallographic analysis of endogenous peptides associated with HLA-DR1 suggests a common, polyproline II-like conformation for bound peptides. Proc. Natl. Acad. Sci. USA 93:734-738.
7. Nigrin, A. 1993. Neural networks for pattern recognition. MIT Press, Cambridge, MA.
8. Beale, R. and Jackson, T. 1990. Neural computing: an introduction. Hilger, Bristol, UK.
9. Weiss, S.M. and Kulikowski, C.A. 1991. Computer systems that learn. Morgan Kaufman Publishers, San Mateo, CA.
10. Brusic, V., Rudy, G., and Harrison, U.C. 1994. Prediction of MHC binding peptides using artificial networks, pp.253-260, in: Complex systems: mechanism of adaptation. Stonier, R. and Yu, X.H. (eds.). IOS Press, Amsterdam, The Netherlands.
11. Brusic, V., Rudy, G., Honeyman, M.C., Hammer, J., and Harrison, L.C. 1998. Prediction of MHC class-II binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14:121-130.
12. Zhang, B., Lan, M.S., and Notkins, A.L. 1997. Autoantibodies to IA-2 in IDDM: location of major antigenic determinants. Diabetes 46:40-43.
13. Tait, BD. and Harrison, L.C. 1991. Overview: the major histocompatlbility complex and insulin-dependent diabetes mellltus, in: Genetics of diabetes part I. Baillere's Clin. Endocrinol. Metab. 5:11-228.
14. Nepom, G.T., Byers, P., Seyfried, C., Healey, L.A., Wilske, K.R., Stage, D. et al. 1989, HLA genes associated with rheumatoid arthritis. Arthritis Rheum. 32:15-21.
15. Hammer, J., Bono, E., Gallazzi, F., Belunls, C., Nagy, Z., and Sinigaglia, R 1994. Precise prediction of MHC class II-peptide interaction based on peptide sidechain scanning. J. Exp. Med. 180:2353-2358.
16. Reece, J.C., Geysen, H.M., and Rodda, S.J. 1993. Mapping the major human T helper epitopes of tetanus toxin. J. Immunol. 151:6175-6184.
17. Pette, M., Fujita K., Wilkinson, D., Altmann, D.M., Trowsdale, J., Giegerich, G. et al. 1990. Myelin autoreactivity in multiple sclerosis: recognition of myelin basic protein In the context of HLA-DR2 products by T lymphocytes of multiple sclerosis patients and healthy donors. Proc. Natl. Acad. Sci. USA 87:7968-7972.
18. Salvetti, M., Jung, S., Chang, S.R, Will, H., Schalke, B.C., and Wekerie, H. 1991. Acetylcholine receptor-specific T lymphocyte clones In the normal human immune repertoire: target epitopes, HLA restriction and membrane phenotypes. Ann. Neurol. 29:508-516.
19. Ashton-Rickardt, P.G. and Tonegawa, S. 1994. A differential-avidity model for T-cell selection. Immunol. Today 15:362-366.
20. Miyata, Y. 1991. A user's guide to PlaNet 5.6. Computer Science Dept, University of Colorado, Boulder, CO.
21. Brusic, V., Rudy, G., Kyne, A.P., and Harrison, L.C. 1997. MHCPEP, a database of MHC-blnding peptides: update 1996. Nucleic Acids Res. 25:269-271.
22. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. 1986. Learning representations by back-propagating errors. Nature 323:533-536.
23. Zurada, J.M. 1992, pp 41-42 in Introduction to artificial neural systems. West Publishing Company, St. Paul, MN.
24. Forrest, S. 1993. Genetic algorithms: principles of natural selection applied to computation. Science 261:872-878.
25. Sinigaglia, F., Romagnoli, P., Guttinger, M., Takacs, B., and Pink, J.R.L. 1991. Selection of T-cell epitopes and vaccine engineering. Methods Enzymol. 203:370-386.
26. Hammer, J., Gallazzi, F., Bono, E., Karr, R., Guenot, J.M., Valsasnini, P. et al. 1995. Peptide binding Specificity of HLA-DR4 molecules: correlation with rheumatoid arthritis association. J. Exp. Med. 181:1847-1855.
27. Harrison, L.C., honeyman, M.C., Trembleau, S., Gregori, S., Gallazzi, F., Augstein, P. et al. 1997. A peptide-binding motif for I-A the class II major histo-Compatibility complex (MHC) molecule of NOD and Biozzi AB/H mice. J. Exp. Med. 185:1013-1021.