Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, Indiana 47907, United States.
J Chem Inf Model. 2011 Oct 24;51(10):2680-9. doi: 10.1021/ci200191m. Epub 2011 Sep 15.
End-point methods such as linear interaction energy (LIE) analysis, molecular mechanics generalized Born solvent-accessible surface (MM/GBSA), and solvent interaction energy (SIE) analysis have become popular techniques to calculate the free energy associated with protein-ligand binding. Such methods typically use molecular dynamics (MD) simulations to generate an ensemble of protein structures that encompasses the bound and unbound states. The energy evaluation method (LIE, MM/GBSA, or SIE) is subsequently used to calculate the energy of each member of the ensemble, thus providing an estimate of the average free energy difference between the bound and unbound states. The workflow requiring both MD simulation and energy calculation for each frame and each trajectory proves to be computationally expensive. In an attempt to reduce the high computational cost associated with end-point methods, we study several methods by which frames may be intelligently selected from the MD simulation including clustering and address the question of how the number of selected frames influences the accuracy of the SIE calculations.
终点法(如线性相互作用能(LIE)分析、分子力学广义 Born 溶剂可及表面积(MM/GBSA)和溶剂相互作用能(SIE)分析)已成为计算蛋白质-配体结合相关自由能的常用技术。这些方法通常使用分子动力学(MD)模拟来生成一组包含结合态和非结合态的蛋白质结构。随后,使用能量评估方法(LIE、MM/GBSA 或 SIE)来计算集合中每个成员的能量,从而提供结合态和非结合态之间平均自由能差的估计值。对于每个帧和轨迹都需要进行 MD 模拟和能量计算的工作流程证明计算成本很高。为了降低终点法相关的高计算成本,我们研究了几种从 MD 模拟中智能选择帧的方法,包括聚类,并探讨了选择的帧数如何影响 SIE 计算的准确性。