Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 312, Issue -, Pages 55-64Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2012.07.020
Keywords
RNA-binding sites; PSSM profile; Torsion angles (phi, psi); Solvent accessible surface area; Support vector machine
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Funding
- National Natural Science Foundation of China [61063016]
- Research Fund for the Doctoral Program of Higher Education of China [20101501110004]
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RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (phi, psi) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins. (C) 2012 Elsevier Ltd. All rights reserved.
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