Albanese Steven K, Chodera John D, Volkamer Andrea, Keng Simon, Abel Robert, Wang Lingle
Louis V. Gerstner, Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.
J Chem Inf Model. 2020 Dec 28;60(12):6211-6227. doi: 10.1021/acs.jcim.0c00815. Epub 2020 Oct 29.
Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.
炼金术自由能计算如今被广泛用于小分子先导物优化中以驱动或维持活性，其精度约为1千卡/摩尔。尽管如此，在已发表的研究中，利用自由能计算来驱动化合物在两个相似靶点间选择性优化的潜力相对未被探索。在最乐观的情况下，结合位点的相似性可能会导致误差的偶然抵消，从而使选择性比亲和力能被更准确地预测。在此，我们评估在小分子激酶抑制剂背景下预测选择性的准确性，考虑人类激酶CDK2和CDK9非常相似的结合位点，以及另一系列试图在亲缘关系更远的激酶CDK2和ERK2之间实现选择性的配体。使用贝叶斯分析方法，我们将系统误差与统计误差分离，并量化选择性靶点间系统误差的相关性。我们发现，在CDK2/CDK9的情况下，系统误差的高度相关性表明自由能计算在帮助化学家实现选择性方面可能有显著影响，而在亲缘关系更远的激酶（CDK2/ERK2）中，系统误差的相关性表明即使在关系不那么紧密的系统之间也可能出现偶然抵消。在这两种情况下，系统误差的相关性都表明更长时间的模拟有利于恰当地平衡统计误差与系统误差，从而充分利用选择性预测中表观自由能计算准确性的提高。
