Abstract

Peptide fragments that serve as the cytotoxic T lymphocyte (CTL) epitopes are processed from antigens by the proteasome and then are transported to the endoplasmic reticulum through transporter associated with antigen processing (TAP) before being loaded onto the MHC class I molecule. Here, we studied TAP specificity by a neighborhood rough set (NRS) model based feature selection and prioritization method. By means of binary, amino acid properties, and binary plus properties of amino acids encoding, respectively, we adopted NRS based feature selection method to select multiple optimal feature sets for TAP binding peptides binary classification. The features in these optimal sets were ranked according to their occurrence frequency. Results show that the NRS is effective for prediction improvement and analysis of the specificity of TAP transporter. The proposed method can be used as a tool for predicting TAP binding peptides and be useful for subunit vaccine rational design and related bioinformatics cases.

Keywords: Feature prioritization, neighborhood rough set, peptide, specificity, SVM, TAP