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/.
No Supplementary Data
Random Forest Classification;
accessible surface area;
protein structure prediction;
Document Type: Research Article
Publication date: 2012-01-01
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Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.