Application of Genetic Search in Derivation of Matrix Models of Peptide Binding to MHC Molecules
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Multi-Objective evolutionary algorithm for discovering peptide binding motifs
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-Ag7 molecule of the non-obese diabetic (NOD) mouse – an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-Ag7 have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all Ag7data sets with high accuracy. A binding score matrix representing peptide binding motif to Ag7was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported motifs.