Improved Prediction of Relative Solvent Accessibility Using Two-stage Support Vector Regression

Predicted relative solvent accessibility (RSA) provides useful information for prediction of binding sites and reconstruction of the 3D-structure based on a protein sequence, which are at the very core of proteomics. Several RSA prediction methods including those that generate real values and those that predict discrete states (buried vs. exposed) have been published. We propose a novel method for real valued prediction that aims to improve the prediction quality when compared with the existing methods. The proposed method combines Support Vector Regression (SVR) predictors into a two-stage architecture. The improved prediction quality comes from a composite sequence representation, which includes a custom-selected subset of features from the PSTBLAST profile, secondary structure predicted with PSTPRED, and binary code that indicates position of a given residue with respect to sequence termini. Based on empirical evaluation with a standard benchmark dataset, the proposed method obtains the mean absolute error (MAE) equal 0.143, which corresponds to 6% error rate reduction when compared with the best performing competing method that obtains 0.152 MAE on this dataset.