Department of Pharmaceutical Chemistry , University of Vienna , UZA 2, Althanstrasse 14 , 1090 Vienna , Austria.
Department of Pharmacy and Biotechnology , Alma Mater Studiorum-Università di Bologna , via Belmeloro 6 , I-40126 Bologna , Italy.
J Chem Inf Model. 2019 Jan 28;59(1):535-549. doi: 10.1021/acs.jcim.8b00614. Epub 2018 Dec 13.
Computational approaches currently assist medicinal chemistry through the entire drug discovery pipeline. However, while several computational tools and strategies are available to predict binding affinity, predicting the drug-target binding kinetics is still a matter of ongoing research. Here, we challenge scaled molecular dynamics simulations to assess the off-rates for a series of structurally diverse inhibitors of the heat shock protein 90 (Hsp90) covering 3 orders of magnitude in their experimental residence times. The derived computational predictions are in overall good agreement with experimental data. Aside from the estimation of exit times, unbinding pathways were assessed through dimensionality reduction techniques. The data analysis framework proposed in this work could lead to better understanding of the mechanistic aspects related to the observed kinetic behavior.
计算方法目前在整个药物发现过程中为药物化学提供辅助。然而，尽管有几种计算工具和策略可用于预测结合亲和力，但预测药物-靶标结合动力学仍然是一个正在研究的问题。在这里，我们挑战大规模分子动力学模拟来评估一系列结构多样的热休克蛋白 90（Hsp90）抑制剂的解吸率，这些抑制剂在其实验停留时间上跨越了 3 个数量级。所得的计算预测与实验数据总体上吻合良好。除了估计退出时间外，还通过降维技术评估了非结合途径。这项工作中提出的数据分析框架可以帮助更好地理解与观察到的动力学行为相关的机制方面。
