Department of Chemistry, University of New Orleans, 2000 Lakeshore Drive, New Orleans, Louisiana 70148, USA.
J Chem Phys. 2012 Dec 21;137(23):230901. doi: 10.1063/1.4769292.
Computational techniques see widespread use in pharmaceutical drug discovery, but typically prove unreliable in predicting trends in protein-ligand binding. Alchemical free energy calculations seek to change that by providing rigorous binding free energies from molecular simulations. Given adequate sampling and an accurate enough force field, these techniques yield accurate free energy estimates. Recent innovations in alchemical techniques have sparked a resurgence of interest in these calculations. Still, many obstacles stand in the way of their routine application in a drug discovery context, including the one we focus on here, sampling. Sampling of binding modes poses a particular challenge as binding modes are often separated by large energy barriers, leading to slow transitions. Binding modes are difficult to predict, and in some cases multiple binding modes may contribute to binding. In view of these hurdles, we present a framework for dealing carefully with uncertainty in binding mode or conformation in the context of free energy calculations. With careful sampling, free energy techniques show considerable promise for aiding drug discovery.
计算技术在药物发现中得到了广泛的应用,但通常在预测蛋白质-配体结合趋势方面不可靠。通过从分子模拟中提供严格的结合自由能,自由能计算寻求改变这一点。在适当的采样和足够准确的力场的情况下,这些技术可以产生准确的自由能估计。自由能计算的最新创新重新引起了人们对这些计算的兴趣。尽管如此,在药物发现背景下,它们的常规应用仍面临许多障碍,包括我们在这里关注的采样问题。结合模式的采样带来了特殊的挑战,因为结合模式通常被大的能量障碍隔开,导致缓慢的转变。结合模式难以预测,在某些情况下,多种结合模式可能有助于结合。有鉴于此,我们提出了一个在自由能计算中处理结合模式或构象不确定性的框架。通过仔细的采样,自由能技术在辅助药物发现方面显示出很大的前景。