Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA.
Nat Commun. 2023 Feb 24;14(1):1060. doi: 10.1038/s41467-023-36720-9.
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 与蛋白质复合物是许多细胞过程的基础，包括基本的翻译和基因调控。高分辨率的 RNA 与蛋白质复合物结构测定对于阐明其功能至关重要。因此，需要能够识别天然样 RNA 与蛋白质结构的计算方法。为了解决这一挑战，我们开发了基于深度学习的 DRPScore 方法，用于识别天然样 RNA 与蛋白质结构。DRPScore 在具有不同结合诱导构象变化程度的 RNA 与蛋白质复合物代表性集合上进行了测试，范围从完全刚性对接（结合态-结合态）到完全柔性对接（未结合态-未结合态）。在测试集中，DRPScore 在结合态 RNA 与蛋白质复合物上选择天然样结构的成功率为 91.67%，在未结合复合物上的成功率为 56.14%。DRPScore 始终优于现有方法，平均提高了 10.53-15.79%，即使对于最困难的未结合情况也是如此。此外，DRPScore 显著提高了天然界面相互作用预测的准确性。DRPScore 应该对 RNA 与蛋白质复合物的建模和设计具有广泛的用途。
