Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach.

Protein interactions with ribonucleic acids (RNA) are well-known to be crucial for a wide range of cellular processes such as transcriptional regulation, protein synthesis or translation, and post-translational modifications. Identification of the RNA-interacting residues can provide insights into these processes and aid in relevant biotechnological manipulations. Owing to their eventual potential in combating diseases and industrial production, several computational attempts have been made over years using sequence- and structure-based information. Recent comparative studies suggest that despite these developments, many problems are faced with respect to the usability, prerequisites, and accessibility of various tools, thereby calling for an alternative approach and perspective supplementation in the prediction scenario. With this motivation, in this paper, we propose the use of a simple-yet-efficient conditional probabilistic approach based on the application of local occurrence of amino acids in the interacting region in a non-numeric sequence feature space, for discriminating between RNA interacting and non-interacting residues. The proposed method has been meticulously tested for robustness using a cross-estimation method showing MCC of 0.341 and F- measure of 66.84%. Upon exploring large scale applications using benchmark datasets available to date, this approach showed an encouraging performance comparable with the state-of-art. The software is available at https://github.com/ABCgrp/DORAEMON.