标题： WeTICA：一种基于加权集合的定向搜索增强采样方法，用于在降低维数空间中估计稀有事件动力学 显示英文标题

标题： WeTICA: A directed search weighted ensemble based enhanced sampling method to estimate rare event kinetics in a reduced dimensional space

摘要： 从分子动力学模拟中估算罕见事件的动力学是一个具有挑战性的任务，尽管增强采样方法已经取得了重大进展。 加权集（WE）模拟是一种特殊的增强采样技术，它提供了一种方法，可以直接从偏差轨迹中计算动力学速率常数，而无需使用偏差势能来修改底层的能量景观。 传统WE算法使用不同的分箱方案将集体变量（CV）空间划分为感兴趣的两个亚稳态。 在这项工作中，我们开发了一种新的“无箱”WE模拟算法，以绕过优化分箱过程的障碍。 我们提出的协议（WeTICA）使用低维CV空间引导WE模拟向指定的目标状态发展。 我们将这种新算法应用于恢复三种蛋白质的展开动力学：(A) TC5b色氨酸笼突变体，(B) TC10b色氨酸笼突变体，和(C) 蛋白质G，其展开时间范围在3到40{\mu }秒之间，使用预定义的固定时间滞后独立成分分析（TICA）特征向量作为CV进行投影。 计算出的展开时间以良好的准确性收敛到报告值，且所需的累积WE模拟时间比展开时间尺度少一个数量级以上，无论是否事先知道能够捕捉展开的CV。 我们的算法可以与其他线性CV一起使用，不限于TICA。 此外，该算法中使用的新的抽样器选择标准可用于更复杂的非线性CV空间，以进一步改进无箱WE方法。 显示英文摘要

摘要： Estimating rare event kinetics from molecular dynamics simulations is a non-trivial task despite the great advances in enhanced sampling methods. Weighted Ensemble (WE) simulation, a special class of enhanced sampling techniques, offers a way to directly calculate kinetic rate constants from biased trajectories without the need to modify the underlying energy landscape using bias potentials. Conventional WE algorithms use different binning schemes to partition the collective variable (CV) space separating the two metastable states of interest. In this work, we have developed a new "binless" WE simulation algorithm to bypass the hurdles of optimizing binning procedures. Our proposed protocol (WeTICA) uses a low-dimensional CV space to drive the WE simulation toward the specified target state. We have applied this new algorithm to recover the unfolding kinetics of three proteins: (A) TC5b Trp-cage mutant, (B) TC10b Trp-cage mutant, and (C) Protein G, with unfolding times spanning the range between 3 and 40 {\mu}s using projections along predefined fixed Time-lagged Independent Component Analysis (TICA) eigenvectors as CVs. Calculated unfolding times converge to the reported values with good accuracy with more than one order of magnitude less cumulative WE simulation time than the unfolding time scales with or without a priori knowledge of the CVs that can capture unfolding. Our algorithm can be used with other linear CVs, not limited to TICA. Moreover, the new walker selection criteria for resampling employed in this algorithm can be used on more sophisticated nonlinear CV space for further improvements of binless WE methods.