Abstract

Determining which peptides bind to a specific major histocompatibility complex (MHC) class I molecule is not only helpful to understand the mechanism of immunity, but also to develop effective anti-tumor epitope vaccines. In order to further study the specificity of MHC class I molecule binding antigen peptide, the support vector regression (SVR) and four amino acid descriptors were used to build four models of predicting binding affinities between peptides and MHC class I molecules. Comparison among performances of the four models indicated that the model based on physicochemical properties of amino acids is more satisfying (AC=75.0%, CC=0.499). Furthermore, the specificities of MHC class I molecule binding antigen peptide were obtained through analysis based on the contribution of the amino acids to peptide-MHC class I molecule binding affinities in the predictive model.

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