Prediction of relative solvent accessibility using pace regression

Abstract

In this paper, a new approach for prediction of protein solvent accessibility is presented. The prediction of relative solvent accessibility gives helpful information for the prediction of native structure of a protein. Recent years several RSA prediction methods including those that generate real values and those that predict discrete states (buried vs. exposed) have been developed. We propose a novel method for real value prediction that aims at minimizing the prediction error when compared with existing methods. The proposed method is based on Pace Regression (PR) predictor. The improved prediction quality is a result of features of PSIBLAST profile and the PR method because pace regression is optimal when the number of coefficients tends to infinity. The experiment results on Manesh dataset show that the proposed method is an improvement in average prediction accuracy and training time.