通过整合实验热力学与动力学数据的Milestoning网络精修
Milestoning network refinement by incorporating experimental thermodynamic and kinetic data
AI总结：
针对Milestoning方法因力场限制难以匹配实验数据的问题，提出以Kullback-Leibler散度率最小化、结合实验热力学与动力学约束的网络精修方法，确保网络扰动最小且符合实验结果，并在β-环糊精配体结合体系中验证。
摘要：
Milestoning是一种用于稀有事件动力学计算的精准高效方法，通过构建连接反应物态与产物态的连续时间动力学网络实现。然而，即便采样充分，其精度仍可能受力场限制，难以与实验数据达成定量一致。为解决该问题，我们提出一种精修方法：在将实验热力学（平衡常数）与动力学（速率常数）数据作为约束的同时，最小化两个Milestoning网络间的Kullback-Leibler散度率。该方法确保精修后的动力学网络相对于原始网络受到的扰动最小，同时满足实验约束。研究以模型主体体系β-环糊精与6种小分子配体的结合-解离动力学为例，验证了该精修方法的有效性。
英文摘要：
Milestoning is an accurate and efficient method for rare event kinetics calculations by constructing a continuous-time kinetic network connecting the reactant and product states. However, even with adequate sampling, its accuracy can also be limited by the force fields, which makes it challenging to achieve quantitative agreement with experimental data. To address this issue, we present a refinement approach by minimizing the Kullback-Leibler divergence rate between two Milestoning networks while incorporating experimental thermodynamic (equilibrium constants) and kinetic (rate constants) data as constraints. This approach ensures that the refined kinetic network is minimally perturbed with respect to the original one, while simultaneously satisfying the experimental constraints. The refinement approach is demonstrated using the binding and unbinding dynamics of a series of six small molecule ligands for the model host system, $β$-cyclodextrin.
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