Prediction of MHC Class II Binding Peptides Using a Multi-Objective Evolutionary Algorithm

Abstract

The identification of T-cell epitopes is important for vaccine development. An epitope is a peptide segment that can bind to both a T-cell receptor and a major histocompatibility complex (MHC) molecule. The prediction of MHC binding peptides is a crucial part of the epitopes identification. This paper presents a novel Multi-Objective Evolutionary Algorithm (MOEA) to predict MHC class II binding peptides. The optimal search strategy of MOEA is used to find a position specific scoring matrix which can present MHC class II binding peptides quantitative motif. The performance of the new algorithm has been evaluated with benchmark datasets