PredRBR: Accurate Prediction of RNA-Binding Residues in proteins using Gradient Tree Boosting

Abstract

Prediction of Protein-RNA binding sites is one of the most challenging and intriguing problems in the field of computational biology. Here, we proposed an effectively machine learning algorithm termed PredRBR (Prediction of RNA Binding Residues), using Gradient Tree Boosting algorithm and mRMR-IFS feature selection method in combination with sequence features, structure characteristics and two categories of structural neighborhood feature for prediction of RNA binding sites in proteins. We evaluate PredRBR on the independent test dataset (RBP101), and obtain significant improvement on the prediction performance compared with other state-of-the-art approaches. In addition, we test the variable importance of diverse feature types. The results show that structural neighborhood features play a crucial role in the identification of RNA binding sites.