On the basis of Bayesian probabilistic inference, Gaussian process (GP) is a powerful machine learning method for nonlinear classification and regression, but has only very limited applications in the new areas of computational vaccinology and immunoinformatics. In the current work, we present a paradigmatic study of using GP regression technique to quantitatively model and predict the binding affinities of over 7000 immunodominant peptide epitopes to six types of human major histocompatibility complex (MHC) proteins. In this procedure, the sequence patterns of diverse peptides are characterized quantitatively and the resulting variables are then correlated with the experimentally measured affinities between different MHC and their peptide ligands, by using a linearity- and nonlinearity-hybrid GP approach. We also make systematical comparisons between the GP and two sophisticated modeling methods as partial least square (PLS) regression and support vector machine (SVM) with respect to their fitting ability, predictive power and generalization capability. The results suggest that GP could be a new and effective tool for the modeling and prediction of MHC-peptide interactions and would be promising in the field of computer-aided vaccine design (CAVD).

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Introduction: It is always a crucial challenge in biotechnology to avoid promiscuous binding between an anticancer peptide and multiple SH3 domains, thus reducing potential toxic effects. In spite of a great deal of experimental efforts, the association between amino acid sequence and binding specificity of peptide remained largely unknown. Aim: The purpose of this study was to optimize the amino acid sequence of peptide ligands and render high specificity towards designated therapeutic targets. Results: By exploring peptide ligands in MINT database and utilizing SH3PepInt tool for in silico peptide-target binding, here we investigated how the amino acid sequence of a peptide determined its specificity of binding to the SH3 domain of c-Src protein. We found that the 5