Two-stage support vector machines to protein relative solvent accessibility prediction

Bioinformatics techniques to relative solvent accessibility (RSA) prediction are mostly single-stage approaches; they predict solvent accessibility of proteins by taking into account only the information available in amino acid sequences. We propose to use support vector machines (SVMs) as a second stage following the existing single-stage approaches for RSA prediction problem to improve the accuracy. The purpose of the second stage is to capture the contextual relationship of solvent accessibility elements in a neighborhood in determining the solvent accessibility at a particular site. We demonstrate our approach by introducing SVMs to the output of single-stage SVM classifier. Two-stage SVM approach achieves accuracies up to 90.4% and 90.2% on the Manesh dataset of 215 protein structures and the RS126 dataset of 126 nonhomologous globular proteins, respectively, which are better than the highest reported scores on both datasets to date.