Predicting protein-protein interaction sites using modified support vector machine

Protein-protein interaction plays a fundamental role in many biological processes and diseases. Characterizing protein interaction sites is crucial for the understanding of the mechanism of protein-protein interaction and their cellular functions. In this paper, we proposed a method based on integrated support vector machine (SVM) with a hybrid kernel to predict-protein interaction sites. First, a number of features of the protein interaction sites were extracted. Secondly, the technique of sliding window was used to construct a protein feature space based on the influence of the adjacent residues. Thirdly, to avoid the impact of imbalance of the data set on prediction accuracy, we employed boost-strap to re-sample the data. Finally, we built a SVM classifier, whose hybrid kernel comprised a Gaussian kernel and a Polynomial kernel. In addition, an improved particle swarm optimization (PSO) algorithm was applied to optimize the SVM parameters. Experimental results show that the PSO-optimized SVM classifier outperforms existing methods.