通过粗粒度模拟和深度学习揭示DDX3X介导的不依赖ATP的熵驱动双链RNA解旋
ATP-Independent Entropy-Driven dsRNA Unwinding by DDX3X Revealed by Coarse-Grained Simulations and Deep Learning
AI总结：
研究DDX3X介导的不依赖ATP使双链RNA解旋的机制，通过构建粗粒度RNA模型及结合深度学习分析，揭示其内在随机性、解旋驱动力及关键中间体，还明确不同熵成分贡献，为研究RNA重塑动力学提供有效框架。
摘要：
传统上，DEAD-box RNA解旋酶（DDXs）被认为是依赖ATP的马达，可解开双链RNA（dsRNA）。然而，最近实验表明，一些DDXs即使在没有ATP的情况下也能促进dsRNA解旋，这引发了关于不依赖ATP的链分离物理机制的基本问题。我们开发了一个基于物理的最小粗粒度RNA模型，并纳入DDX3X与dsRNA之间的弱特异性相互作用，揭示了解旋事件的内在随机性。解旋过程必须克服能量障碍，但热涨落和熵增为RNA重塑提供了驱动力。我们确定dsRNA分离通过DDX3X促进的罕见但必要的链置换中间体进行。通过结合深度学习辅助分析，我们进一步对不同熵成分的贡献进行排名，揭示氢键是主导因素，其次是碱基堆积和主链构象。这些发现揭示了一种以前未被认识的RNA双链解旋物理机制，并为研究RNA重塑动力学提供了一个有效的框架。
英文摘要：
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 dominate 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.
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