Predicting Binding Peptides with Simultaneous Optimization of Entropy and Evolutionary Distance
Identifying antigenic peptides that bind to Major Histocompatibility Complex (MHC) molecules plays a central role in determining T-cell epitopes suitable as vaccine targets. Prediction of the binding ability of antigenic peptides to MHC class II molecules is more complex that for class I. Class II molecules bind to peptides of different lengths and the core region that interacts with the binding site on the class II MHC molecule is located anywhere within the peptide. Obtaining an alignment for these binding sites is an important first step in determining the binding motif of MHC class II alleles. In this paper, we exploit entropy and evolutionary distance of the key binding positions (anchor positions) of an alignment in determining the best possible alignment for a given set of peptide data. Once an optimal alignment is found, a weight matrix representing the binding motif is estimated. The weight matrix designed is subsequently applied to predict MHC binding peptides.
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