AI 中文总结
研究膜融合中柄形成这一关键步骤，采用基于反应坐标函数的增强采样策略，通过机器学习获取有效集体变量，实现均匀采样，获得自由能估计和柄形成机理洞察。
AI 中文摘要
膜融合对于细胞通讯和功能至关重要，理解两个脂质双层如何融合是制定治疗策略的关键。功能化纳米颗粒最近成为合成融合剂，但驱动这一过程的分子机制仍不清楚，部分原因是融合涉及跨越高自由能垒的转变，难以在分子模拟中捕捉。虽然增强采样方法可以解决这个问题，但它们也依赖于集体变量的定义，而对于融合来说，集体变量特别难以定义，因为它源于许多分子的集体重排，不易简化为简单直观的坐标。在这里，我们通过采用基于反应坐标函数的增强采样策略来研究由两亲性金纳米颗粒介导的融合的第一步——柄形成，该反应坐标函数通过自洽过程进行机器学习。这种方法只需要对系统有最少的先验知识，并利用学习到的反应坐标函数作为有效的集体变量，从而能够对整个路径进行均匀采样。从得到的反应轨迹和广泛的过渡区域采样中，我们获得了收敛的自由能估计和对柄形成的机理洞察。
英文摘要
Membrane fusion is essential for cellular communication and function, and understanding how two lipid bilayers merge is key to informing therapeutic strategies. Functionalized nanoparticles have recently emerged as synthetic fusogens, but the molecular mechanisms driving this process remain unclear, partly because fusion involves transitions over high free-energy barriers, difficult to capture in molecular simulations. While enhanced sampling methods can address this problem, they also rely on the definition of collective variables, which are especially hard to define for fusion, as it arises from the collective rearrangement of many molecules and cannot be easily reduced to a simple intuitive coordinate. Here, we study stalk formation, the first step of fusion, mediated by an amphiphilic gold nanoparticle, by employing an enhanced sampling strategy based on the committor function, machine-learned through a self-consistent procedure. This method requires minimal prior knowledge of the system and leverages the learned committor function as an effective collective variable, enabling uniform sampling of the entire pathway. From the resulting reactive trajectories and extensive transition region sampling, we obtain converged free-energy estimates and mechanistic insight into stalk formation.
