A conformational epitope prediction system based on sequence and structural characteristics

Abstract

An epitope is composed of several amino acids located on structural surface of an antigen. These gathered amino acids can be specifically recognized by antibodies, B cells, or T cells through immune responses. Precise recognition of epitopes plays an important role in immunoinformatics for vaccine design applications. Conformational epitope (CE) is the major type of epitopes in a vertebrate organism, but neither regular combinatorial patterns nor fixed geometric features are known for a CE. In this paper, a novel CE prediction system was established based on physico-chemical propensities of sequence contents, spatial geometrical conformations, and surface rates of amino acids. In addition, a support vector machine technique was also applied to train appearing frequencies of combined neighboring surface residues of known CEs, and it was applied to classify the best predicted CE candidates. In order to evaluate prediction performance of the proposed system, an integrated dataset was constructed by removing redundant protein structures from current literature reports, and three testing datasets from three different systems were collected for validation and comparison. The results have shown that our proposed system improves in both specificity and accuracy measurements. The performance of average sensitivity achieves 36%, average specificity 92%, average accuracy 89%, and average positive predictive value 25%.