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A neural network based MHC Class-I Binding Peptide Prediction Server
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The list of all the publication references which are useful in the development of this server.The reference for quantitative matrices, motifs, neural network and development of the algorithm are mentioned below.

  • Adams,H.P. and Koziol,J.A.(1995) Prediction of binding to MHC class I molecules. J Immunol Methods.,185,181-90.

  • Ayyoub, M., Stevanovic, S., Sahin, U., Guillaume, P., Servis, C., Rimoldi, D., Valmori, D., Romero, P., Cerottini, J.C., Rammensee, HG., Pfreundschuh, M., Speiser, D. and Levy, F. (2002) Proteasome-assisted identification of a SSX-2-derived epitope recognized by tumor-reactive CTL infiltrating metastatic melanoma. J. Immunol., 168, 1717-22.

  • Bhasin,M., Singh,H. and Raghava,G.P.S. (2001) MHCBN: A comprehensive database of MHC binding and non-binding peptides. Nucleic Acid Res., (Online,

  • Brusic V., Rudy G. and Harrison L.C. (1995). Prediction of MHC binding peptides using artificial neural networks. Complexity International 2, 1995,

  • 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(2), 121-130.

  • Buus S. Description and prediction of peptide-MHC binding: the 'human MHC project'.(1999) Curr Opin Immunol., 11209-13. Review.

  • Doytchinova, I.A. and Flower, D.R. (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.

  • Gulukota,K., Sidney,J., Sette,A. and DeLisi, C. Two complementary methods for predicting peptides binding major histocompatibility complex molecules.(1997) J Mol Biol., 267,1258-67.

  • Goldberg. A., Cascio, P., Saric, T. and Rock K. (2002) The importance of the proteasome and subsequent proteolytic steps in the generation of antigenic peptides. Mol Immunol., 39,147-164.

  • Honeyman M.C., Brusic V., Stone N. and Harrison L.C. (1998). Neural network-based prediction of candidate T-cell epitopes Nature Biotechnology, 16(10), 966-969.

  • Hertz, J.A., Palmer, R.G. and Krogh, A.S. (1991) Introduction to theory of neural computation. Addison-wesley, Redwood city.

  • Holzhutterer, HG., Frommel, C. and Kloetzel, PM. (1999) A theoretical approach towards the identification of cleavage-determining amino acid motifs of the 20 S proteasome. J. Mol. Biol., 286, 1251-65.

  • Kessler, J.H., Beekman, N.J., Bres-Vloemans, S.A., Verdijk, P., vanVeelen, P.A., Kloosterman-Joosten, A.M., Vissers, D.C.J., ten Bosch, G.J.A., Kester, M.G.D., Sijts, A., Drijfhout, J.W., Ossendrop,F., Offringa, R. and Melief, C.J.M. (2001) Efficient identification of novel HLA-A*0201-presented cytotoxic T lymphocyte epitopes in the widely expressed tumor antigen PRAME by proteasome-mediated digestion analysis. J. Exp. Med., 193, 73-88.

  • Kondo, A., Sidney, J., Southwood, S., del Guercio, MF., Appella, E., Sakamoto, H., Celis,E., Grey, HM., Chesnut, RW., Kubo, RT. et al.(1995) Prominent roles of secondary anchor residues in peptide binding to HLA-A24 human class I molecules. J Immunol., 155, 4307-12.

  • Nussbaum, A.K., Kuttler, C., Hadeler, K.P., Rammensee, HG. and Schild, H. (2001) PAProC: a prediction algorithm for proteasomal cleavages available on the WWW. Immunogenetics, 53,87-94.

  • Parker,K.C., Bednarek,M.A. and Coligan,J.E. (1994). Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol., 152, 163-75.

  • Rammensee,H.G, Bachmann,J., Emmerich,N.P., Bachor,O.A. and Stevanovic,S. (1999). SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50, 213-9.

  • Ruppert,J., Sidney,J., Celis,E., Kubo,R.T., Grey,H.M. and Sette,A. (1993) Prominent role of secondary anchor residues in peptide binding to HLA-A2.1 molecules. Cell. 74, 929-937.

  • Schirle,M., Weinschenk.T. and Stevanovic,S.(2001) Combining computer algorithms with experimental approaches permits the rapid and accurate identification of T cell epitopes from defined antigens. J Immunol Methods., 257,1-16. Review.

  • Schönbach C., Yu K., Brusic V.(2002) Large-scale computational identification of HIV T-cell epitopes.Immunology and Cell Biology, 80, 300-306.

  • Schueler-Furman, O., Altuvia, Y., Sette, A. and Margalit, H.(2000) Structure-based prediction of binding peptides to MHC class I molecules:application to a broad range of MHC alleles. Protein Sci,. 9,1838-46.

  • Sidney,J., Southwood,S., Guercio,M.D., Grey,H.M., Chesnut,R.W., Kubo,R.T. and Sette,A. (1996) Specificity and degeneracy in peptide binding to HLA-B7 like class 1 molecules. J. Immunol., 157, 3480-3490.

  • Singh,H. and Raghava,G.P.S. (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics, 17, 1236-1237.

  • Toes, RE., Nussbaum, AK., Degermann, S., Schirle, M., Emmerich,N.P.N., Kraft,M. Laplace, C., Zwinderman, A., Dick, TP., Muller, J., Schonfisch, B., Schmid, C., Fehling, HJ., Stevanovic, S., Rammensee, HG., Schild, H. (2001) Discrete cleavage motifs of constitutive and immunoproteasomes revealed by quantitative analysis of cleavage products. J. Exp. Med., 194, 1-12.

  • Zell, A., and Mamier, G. (1997) Stuttgart Neural Network Simulator version 4.2 university of Stuttgart.

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