RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method

Ganesan      Pugalenthi; Krishna      Kumar Kandaswamy; Kuo-Chen      Chou; Saravanan      Vivekanandan; Prasanna      Kolatkar

Abstract

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/.

Keywords: accessible surface area, protein interface, hydrophobic core, functional residue, conserved residue, protein structure prediction, RSARF, thresholds, Solvent accessibility, JOY suite, MSA, Feature space, Random Forest Classification, OOB, Performance measures