Radial Basis Function Neural Network Ensemble for Predicting Protein-Protein Interaction Sites in Heterocomplexes

Abstract

Prediction of protein-protein interaction sites can guide the structural elucidation of protein complexes. We propose a novel method using a radial basis function neural network (RBFNN) ensemble model for the prediction of protein interaction sites in heterocomplexes. We classified protein surface residues into interaction sites or non-interaction sites based on the RBFNNs trained on different datasets, then judged a prediction to be the final output. Only information of evolutionary conservation and spatial sequence profile are used in this ensemble predictor to describe the protein sites. A non-redundant data set of heterodimers used is consisted of 69 protein chains, in which 10329 surface residues can be found. The efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.689, the sensitivity of 66.6% and the specificity of 67.6%.