Prediction of Protein-RNA Binding Sites by a Random Forest Method with Combined Features
Dr. Luonan Chen
Department of Electronics, Information and Communication Engineering
Osaka Sangyo University
Professor, Executive Director
Key Laboratory of Systems Biology
Chinese Academy of Sciences
Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function, and eukaryotic spliceosomes. As a result, a reliable identification of RNA-binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide. In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence and structure based features. Specifically, the interaction propensity represents nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein-RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85, and AUC of 0.92 prediction of the RNA-binding residues for a dataset containing 205 non-homologous RNA-binding proteins, and also outperforms the existing methods in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein-RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues.
About the Speaker: Prof. Luonan Chen received the M.E. and Ph.D. degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1988 and 1991, respectively. During 1997–2010, he was an associate professor and then professor in the Department of Electrical Engineering and Electronics at Osaka Sangyo University, Osaka, Japan. He was the founding director of the Institute of Systems Biology, Shanghai University, and is currently Executive Director of Key Laboratory of Systems Biology, Chinese Academy of Sciences, and also a research professor at The University of Tokyo. His fields of interest are systems biology, bioinformatics, and nonlinear dynamics. Prof. Chen serves as an editor and editorial board member for many international journals related to systems biology, e.g., associate editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, associate editor of BMC Systems Biology, and editorial board member of IET Systems Biology, Systems Science and Complexity and International Journal of Systems and Synthetic Biology, respectively. In the last six years, he published over 100 journal papers and two monographs related to systems biology and computational biology. He is also the founding chair of Technical Committee on Systems Biology in IEEE SMC Society, and also the founding president of the Computational Systems Biology Society of ORS China.
Wednesday, April 28, 2010
2:30 p.m.–4:00 p.m.
Department Conference Room