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In Frontiers in molecular biosciences

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein-RNA interactions are still poorly derstood in contrast to protein-protein and protein-DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Bheemireddy Sneha, Sandhya Sankaran, Srinivasan Narayanaswamy, Sowdhamini Ramanathan


RNA-protein complex, RNA-protein interaction, RPI, computational prediction, deep learning, interface prediction, machine learning