Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.
BACKGROUND
RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.


RESULTS
In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631.


CONCLUSIONS
The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Meijian
M
Sun
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Xia
X
Wang
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Chuanxin
C
Zou
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Zenghui
Z
He
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China.
Wei
W
Liu
State Key Laboratory of Bioreactor Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai, 200237, China. hlli@ecust.edu.cn.
Honglin
H
Li
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis*; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D016601:RNA-Binding Proteins / Q000737:chemistry*; D012372:ROC Curve; D015203:Reproducibility of Results; D055672:Static Electricity
D000596:Amino Acids; D016601:RNA-Binding Proteins; D012313:RNA
Protein-RNA interactions; Random forest classifier; Residue electrostatic surface potential; Residue triplet interface propensity; Structural analysis