论文标题
来自短条设备数据的长时间尺度预测：TRP笼小蛋白的基准分析
Long-timescale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniprotein
论文作者
论文摘要
由于许多有助于集体运动的自由度，阐明具有分子动力学模拟的统计信心的物理机制可能会挑战。为了解决这个问题，我们最近引入了动力学盖金近似（DGA）[Thiede等。 J. Phys。化学150，244111（2019）]，其中，满足动力运算符方程的化学动力学统计是由基础扩展表示的。在这里，我们重新制定了这种方法，澄清（并减少）对滞后时间选择的依赖。我们向反应电流提出了一个新的预测，这些预测在集体变量上，并为利率和委员会提供了改进的估计量。我们还提出了简单的程序，用于从任意分子特征构建合适的平滑基础函数。为了通过数值评估估计器和基础设置，我们生成并仔细验证了一个简短轨迹的数据集，以进行TRP-CAGE微蛋白的展开和折叠，这是一个良好的系统。我们的分析证明了一种综合策略，用于定量地表征反应途径。
Elucidating physical mechanisms with statistical confidence from molecular dynamics simulations can be challenging owing to the many degrees of freedom that contribute to collective motions. To address this issue, we recently introduced a dynamical Galerkin approximation (DGA) [Thiede et al. J. Phys. Chem. 150, 244111 (2019)], in which chemical kinetic statistics that satisfy equations of dynamical operators are represented by a basis expansion. Here, we reformulate this approach, clarifying (and reducing) the dependence on the choice of lag time. We present a new projection of the reactive current onto collective variables and provide improved estimators for rates and committors. We also present simple procedures for constructing suitable smoothly varying basis functions from arbitrary molecular features. To evaluate estimators and basis sets numerically, we generate and carefully validate a dataset of short trajectories for the unfolding and folding of the trp-cage miniprotein, a well-studied system. Our analysis demonstrates a comprehensive strategy for characterizing reaction pathways quantitatively.
