Overcoming translational barriers in RNA-protein docking: enhancing computational accuracy for targeted drug discovery.

Abstract

RNA-protein interactions can play a crucial role in the regulation of gene expression, cellular processes, and progression of diseases, thus making them one of the major targets for drug discovery. Although knowledge of these complex interactions remains limited, owing to less structural resolution data, computational, and translational challenges. The review overviews the evolution of advanced computational docking tools and recent cutting-edge innovations in RNA-protein interaction research, by highlighting advanced and highly precise approaches such as cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR) spectroscopy, and novel molecular docking models like DiffDock. Furthermore, the integration of multi-omics data and machine learning approaches in drug discovery not only improves precision but also the speed and efficiency of docking, thus highlighting the dynamic and highly complex nature of RNA molecules. The major translational hurdles that limit the bridging between computational predictions and clinical applications are also highlighted, thus demanding more interdisciplinary collaborations to achieve the desired biomolecular targets. By emphasizing computational modeling, structural biology, clinical pharmacology, and translational barriers in RNA-protein docking, the article provides a comprehensive framework to speed up the highly specific, accurate, and precise drug discovery of novel therapeutics targeting RNA-protein interactions.