Prediction of protein-RNA interactions using sequence and structure descriptors

Protein-RNA interactions play critical roles in numerous biological processes such as posttranscriptional regulation and protein synthesis. However, experimental screening of protein-RNA interactions is usually laborious and time-consuming. It is therefore desirable to develop efficient bioinformatics methods to predict protein-RNA interactions, which can provide valuable hints for future experimental design and advance our understanding of the interaction mechanisms. In this study, we propose a novel method for predicting protein-RNA interactions based on both sequence and structure descriptors of protein and RNA (e.g., the sequence-based physicochemical features, the secondary and three-dimensional structure based features). We train and compare several classifiers using these descriptors on several benchmark datasets, and the random forest method is selected to build an efficient predictor of protein-RNA interactions. We conduct further cross-validation and case studies, and the results clearly suggest the efficacy of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.