RBRDetector: improved prediction of binding residues on RNA-binding protein structures using complementary feature- and template-based strategies.

Computational prediction of RNA-binding residues is helpful in uncovering the mechanisms underlying protein-RNA interactions. Traditional algorithms individually applied feature- or template-based prediction strategy to recognize these crucial residues, which could restrict their predictive power. To improve RNA-binding residue prediction, herein we propose the first integrative algorithm termed RBRDetector (RNA-Binding Residue Detector) by combining these two strategies. We developed a feature-based approach that is an ensemble learning predictor comprising multiple structure-based classifiers, in which well-defined evolutionary and structural features in conjunction with sequential or structural microenvironment were used as the inputs of support vector machines. Meanwhile, we constructed a template-based predictor to recognize the putative RNA-binding regions by structurally aligning the query protein to the RNA-binding proteins with known structures. The final RBRDetector algorithm is an ingenious fusion of our feature- and template-based approaches based on a piecewise function. By validating our predictors with diverse types of structural data, including bound and unbound structures, native and simulated structures, and protein structures binding to different RNA functional groups, we consistently demonstrated that RBRDetector not only had clear advantages over its component methods, but also significantly outperformed the current state-of-the-art algorithms. Nevertheless, the major limitation of our algorithm is that it performed relatively well on DNA-binding proteins and thus incorrectly predicted the DNA-binding regions as RNA-binding interfaces. Finally, we implemented the RBRDetector algorithm as a user-friendly web server, which is freely accessible at http://ibi.hzau.edu.cn/rbrdetector.