Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.
Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
J Comput Aided Mol Des. 2018 Oct;32(10):1001-1012. doi: 10.1007/s10822-018-0149-3. Epub 2018 Aug 23.
Interest in ligand binding kinetics has been growing rapidly, as it is being discovered in more and more systems that ligand residence time is the crucial factor governing drug efficacy. Many enhanced sampling methods have been developed with the goal of predicting ligand binding rates ([Formula: see text]) and/or ligand unbinding rates ([Formula: see text]) through explicit simulation of ligand binding pathways, and these methods work by very different mechanisms. Although there is not yet a blind challenge for ligand binding kinetics, here we take advantage of experimental measurements and rigorously computed benchmarks to compare estimates of [Formula: see text] calculated as the ratio of two rates: [Formula: see text]. These rates were determined using a new enhanced sampling method based on the weighted ensemble framework that we call "REVO": Reweighting of Ensembles by Variance Optimization. This is a further development of the WExplore enhanced sampling method, in which trajectory cloning and merging steps are guided not by the definition of sampling regions, but by maximizing trajectory variance. Here we obtain estimates of [Formula: see text] and [Formula: see text] that are consistent across multiple simulations, with an average log10-scale standard deviation of 0.28 for on-rates and 0.56 for off-rates, which is well within an order of magnitude and far better than previously observed for previous applications of the WExplore algorithm. Our rank ordering of the three host-guest pairs agrees with the reference calculations, however our predicted [Formula: see text] values were systematically lower than the reference by an average of 4.2 kcal/mol. Using tree network visualizations of the trajectories in the REVO algorithm, and conformation space networks for each system, we analyze the results of our sampling, and hypothesize sources of discrepancy between our [Formula: see text] values and the reference. We also motivate the direct inclusion of [Formula: see text] and [Formula: see text] challenges in future iterations of SAMPL, to further develop the field of ligand binding kinetics prediction and modeling.
人们对配体结合动力学的兴趣迅速增长，因为越来越多的系统发现，配体停留时间是决定药物疗效的关键因素。为了预测配体结合速率([Formula: see text])和/或配体解吸速率([Formula: see text])，已经开发了许多增强采样方法([Formula: see text])，这些方法通过明确模拟配体结合途径来实现，并且这些方法的工作机制非常不同。尽管目前还没有针对配体结合动力学的盲测挑战，但在这里，我们利用实验测量和严格计算的基准来比较通过两种速率之比([Formula: see text])计算得出的[Formula: see text]的估计值。这些速率是使用一种新的基于加权集合框架的增强采样方法确定的，我们称之为“REVO”：通过方差优化对集合进行重新加权。这是 WExplore 增强采样方法的进一步发展，其中轨迹克隆和合并步骤不是由采样区域的定义来指导，而是通过最大化轨迹方差来指导。在这里，我们获得了多个模拟中一致的[Formula: see text]和[Formula: see text]的估计值，ON 速率的平均对数尺度标准偏差为 0.28，OFF 速率的平均对数尺度标准偏差为 0.56，这在一个数量级内，并且比以前 WExplore 算法的应用要好得多。我们对三个主体-客体对的排序与参考计算一致，然而，我们预测的[Formula: see text]值比参考值平均低 4.2 kcal/mol。我们使用 REVO 算法中轨迹的树状网络可视化和每个系统的构象空间网络来分析我们采样的结果，并假设我们的[Formula: see text]值与参考值之间存在差异的原因。我们还提出将[Formula: see text]和[Formula: see text]挑战直接纳入未来的 SAMPL 迭代中，以进一步发展配体结合动力学预测和建模领域。
