期刊
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
卷 9, 期 17, 页码 4941-4948出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.8b02047
关键词
-
类别
资金
- Director's New Innovator Award Program NIH [DP2-OD007237]
- National Biomedical Computation Resource (NBCR) [NIH P41-GM103426, TG-CHE060073]
- NIH Molecular Biophysics Training Program [T32-GM008326]
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P41GM103426, T32GM008326] Funding Source: NIH RePORTER
Efficient prediction and ranking of small molecule binders by their kinetic (Icon and k(off)) and thermodynamic (Delta G) properties can be a valuable metric for drug lead optimization, as these quantities are often indicators of in vivo efficacy. We have previously described a hybrid molecular dynamics, Brownian dynamics, and milestoning model, Simulation Enabled Estimation of Kinetic Rates (SEEKR), that can predict icon's, k(off)(')s, and Delta G's. Here we demonstrate the effectiveness of this approach for ranking a series of seven small molecule compounds for the model system, beta-cyclodextrin, based on predicted k(on)(')s and k(off)(')s. We compare our results using SEEKR to experimentally determined rates as well as rates calculated using long time scale molecular dynamics simulations and show that SEEKR can effectively rank the compounds by k(off) and Delta G with reduced computational cost. We also provide a discussion of convergence properties and sensitivities of calculations with SEEKR to establish best practices for its future use.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据