LRC: A New Algorithm for Prediction of Conformational B-cell Epitopes Using Statistical Approach and Clustering Method

Identifying of B-cell epitopes from antigen is a challenging task in bioinformatics and applied in vaccine design and drug development. Recently, several methods have been presented to predict epitopes. The physicochemical or structural properties are used by these methods. In this paper, we propose a more appropriate epitope prediction method, LRC, that is based on a combination of physicochemical and structural properties. First, we construct a graph from the surface of antigen, then by using the logistic regression, we model the physicochemical and structural properties and weight each vertex of the graph. Finally, we utilize a clustering method, MCL, to cluster the graph. The effectiveness of the proposed method is benchmarked using several antibody-antigen PDB complexes. The results of LRC algorithm are compared with other methods (DiscoTope, SEPPA and Ellipro) in terms of sensitivity, specificity and other well-known measures. Results indicate that applying the LRC algorithm improves the precision of prediction epitopes in comparison with the mentioned methods. Our LRC program and supplementary material are freely available from http://bs.ipm.ir/softwares/LRC/.