B-Cell Conformational Epitope Prediction based on Structural Relationship and Antigenic Characteristics

Conformational Epitopes (CEs) are composed of discontinuous segments folding with polypeptide chains, which play an important role in vaccine designs and immuno-biological experiments. It has been estimated that more than 90% of B-cell epitopes are in a conformational structure, and therefore prediction of CEs becomes an important task for practical applications. The proposed system is the first tool that adopts three-dimensional mathematical morphology to define protein structural surface regions. Based on the surface information, spatial distance relationship and antigenic characteristics of amino acids are considered as key features for CEs candidate identification. In this paper, thirty three antigen-antibody complexes with verified CEs were adopted to evaluate prediction accuracy, and the performance was compared with PEPOP. To observe the influence of chemical property scales and surface ratios, a complete evaluation by various parameter settings were performed as well. It was discovered that the sensitivity was improved by selecting hydrophilicity rather than hydrophobicity features, while the specificity favored using hydrophobicity instead of hydrophilicity feature in general. The prediction results of the proposed systemachieves an overall accuracy of 75% and its performance is comparable to existing tools.