Nature Biotechnology
16, 753 - 756 (1998)
doi:10.1038/nbt0898-753
Application of an artificial neural network to predict specific class I MHC binding peptide sequencesMariusz Milik1, Dean Sauer1, Anders P. Brunmark1, Lunli Yuan1, Antonella Vitiello1, Michael R. Jackson1, Per A. Peterson1, Jeffrey Skolnick2
& Charles A. Glass1, *
1R. W. Johnson Pharmaceutical Research Institute, San Diego, CA 92121.
2Department of Molecular Biology, The Scripps Research Institute, La Jolla, CA 92037.
*(e-mail: cglass@prius.jnj.com). Computational methods were used to predict the sequences of peptides that bind to the MHC class I molecule, Kb. The rules for predicting binding sequences, which are limited, are based on preferences for certain amino acids in certain positions of the peptide. It is apparent though, that binding can be influenced by the amino acids in all of the positions of the peptide. An artificial neural network (ANN) has the ability to simultaneously analyze the influence of all of the amino acids of the peptide and thus may improve binding predictions. ANNs were compared to statistically analyzed peptides for their abilities to predict the sequences of Kb binding peptides. ANN systems were trained on a library of binding and non-binding peptide sequences from a phage display library. Statistical and ANN methods identified strong binding peptides with preferred amino acids. ANNs detected more subtle binding preferences, enabling them to predict medium binding peptides. The ability to predict class I MHC molecule binding peptides is useful for immunolological therapies involving cytotoxic-T cells.
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