Deriving Matrix of Peptide-MHC Interactions in Diabetic Mouse by Genetic Algorithm

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-A molecule of the non-obese diabetic (NOD) mouse an animal model for insulindependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A 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 A data sets with high accuracy. A binding score matrix representing peptide binding motif to A was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported motifs.