粗粒化模拟结合深度学习揭示 DDX3X 不依赖 ATP 的熵驱动 dsRNA 解链机制
ATP-independent entropy-driven dsRNA unwinding by DDX3X revealed by coarse-grained simulations and deep learning
研究用基于物理的粗粒化 RNA 模型，并加入 DDX3X 与 dsRNA 之间的弱特异性相互作用，模拟 DDX3X 在不依赖 ATP 的情况下解开 dsRNA 的过程。结果显示解链事件具有内在随机性，需要越过一个能垒，而热涨落与熵增提供 RNA 重塑的驱动力；解链通过罕见但必需的链置换中间态完成。作者结合深度学习辅助分析，对不同熵组分的贡献排序，发现氢键作用占主导，其次是碱基堆积，再次是骨架构象。该工作提出一种此前未被认识的 RNA 双链解链物理机制，并为研究 RNA 重塑动力学提供框架。
来源：PLOS Computational Biology · journals.plos.org
by Kang Wang, Chun-Lai Ren, Yu-Qiang Ma
DEAD-box RNA helicases (DDXs) are traditionally known as ATP-dependent motors that unwind double-stranded RNA (dsRNA). Recent experiments, however, show that some DDXs promote dsRNA unwinding even in the absence of ATP, raising a fundamental question about the physical mechanism underlying ATP-independent strand separation. Here, we develop a minimal, physics-based coarse-grained RNA model and incorporate weak, specific interactions between DDX3X and dsRNA, revealing the inherently stochastic nature of unwinding events. The unwinding process must overcome an energy barrier, but thermal fluctuations and entropy gain provide a driving force for RNA remodeling. We identify that dsRNA separation proceeds through rare yet obligatory strand-displacing intermediates facilitated by DDX3X. By combining deep learning-assisted analysis, we further rank the contributions of different entropic components, revealing hydrogen bonding as the dominant factor, followed by base stacking and then the backbone conformation. These findings reveal a previously unrecognized physical mechanism for RNA duplex unwinding and offer an effective framework for studying RNA remodeling kinetics.