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

Research output: Contribution to journalArticle

45 Citations (Scopus)


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 SUPPL.ementary data are available at

Original languageEnglish
Pages (from-to)50-56
Number of pages7
JournalProtein and Peptide Letters
Issue number1
Publication statusPublished - Jan 2012


  • Accessible surface area
  • Conserved residue
  • Functional residue
  • Hydrophobic core
  • Protein interface
  • Protein structure prediction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry

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