PRINTR: prediction of RNA binding sites in proteins using SVM and profiles.
Protein-RNA interactions play a key role in a number of biological processes such as protein synthesis, mRNA processing, assembly and function of ribosomes and eukaryotic spliceosomes. A reliable identification of RNA-binding sites in RNA-binding proteins is important for functional annotation and site-directed mutagenesis. We developed a novel method for the prediction of protein residues that interact with RNA using support vector machine (SVM) and position-specific scoring matrices (PSSMs). Two cases have been considered in the prediction of protein residues at RNA-binding surfaces. One is given the sequence information of a protein chain that is known to interact with RNA; the other is given the structural information. Thus, five different inputs have been tested. Coupled with PSI-BLAST profiles and predicted secondary structure, the present approach yields a Matthews correlation coefficient (MCC) of 0.432 by a 7-fold cross-validation, which is the best among all previous reported RNA-binding sites prediction methods. When given the structural information, we have obtained the MCC value of 0.457, with PSSMs, observed secondary structure and solvent accessibility information assigned by DSSP as input. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/printr/ .
Wang
Y
Y
Institute of Biophysics and Biochemistry, School of Life Science, Huazhong University of Science and Technology, Wuhan City, China. yanw@mail.hust.edu.cn
Xue
Z
Z
Shen
G
G
Xu
J
J
D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D011506:Proteins / Q000737:chemistry*; D012313:RNA / Q000737:chemistry*; D016415:Sequence Alignment
D011506:Proteins; D012313:RNA