Evaluating native-like structures of RNA-protein complexes through the deep learning method

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes. RNA-protein docking is a very challenging area. Here, the authors develop a deep-learning based method, DRPScore, to evaluate RNA-protein complexes. DRPScore is robust and consistently performs better than existing methods on representative testing sets.

Automated summary

RNA-protein complexes play vital roles in various cellular processes. The authors develop a deep-learning-based method called DRPScore to identify native-like structures of RNA-protein complexes. DRPScore outperforms existing methods and shows significant improvements in accuracy, especially for unbound cases.