Motivation: Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Results: Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. Availability and implementation: The PrabHot webserver is freely available at http://denglab.org/PrabHot/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Identifying the interactions between proteins and Long non-coding RNAs (lncRNAs) can provide valuable clues for understanding the mechanisms and physiological functions of lncRNAs. In this work, we propose a computational method, PLIPCOM, which can accurately detect protein-lncRNA interactions by integrating two groups of network features. Low dimensional diffusion characteristics and HeteSim features are combined to build the protein-lncRNA interaction predictor using the Gradient Tree Boosting (GTB) algorithm. In a cross-validation experiment on the benchmark data set, our PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting the interactions between proteins and lncRNAs.