An Implicit Solvation Model for Binding Free Energy Estimation in Nonaqueous Solution.

IF 2.9 2区 化学 Q3 CHEMISTRY, PHYSICAL The Journal of Physical Chemistry B Pub Date : 2025-02-13 Epub Date: 2025-01-29 DOI:10.1021/acs.jpcb.4c08592
David Elsing, Wolfgang Wenzel, Mariana Kozlowska
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Abstract

Implicit solvation models permit the approximate description of solute-solvent interactions, where water is the most often considered solvent due to its relevance in biological systems. The use of other solvents is less common but is relevant for applications such as in nuclear magnetic resonance (NMR) or chromatography. As an example, chloroform is commonly used in anisotropic NMR to measure residual dipolar couplings (RDCs) of chiral analytes weakly aligned by an alignment medium. They can be calculated from molecular dynamics (MD) simulations with explicit solvent, but it is computationally expensive, because tens of microseconds-long MD trajectories should be collected. Here, we develop a computational protocol and numerical implementation for binding free energies of rigid organic molecules to poly-γ-benzyl-l-glutamate using an implicit solvation model of chloroform. The model parameters are fit to alchemical binding free energies obtained from MD simulations in explicit chloroform and compared to the MD results and another implicit solvation model. Possible applications of the method are docking or Monte Carlo simulations based on a physically meaningful scoring function for the fast prediction of interaction poses of ligands for selective binding or the alignment of analytes.

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非水溶液中结合自由能估计的隐式溶剂化模型。
隐式溶剂化模型允许溶质-溶剂相互作用的近似描述,其中水是由于其在生物系统中的相关性而最常被认为是溶剂。其他溶剂的使用不太常见,但与核磁共振(NMR)或色谱等应用相关。例如,氯仿通常在各向异性核磁共振中用于测量手性分析物在取向介质弱取向下的残余偶极耦合(rdc)。它们可以用显式溶剂从分子动力学(MD)模拟中计算出来,但计算成本很高,因为需要收集数十微秒长的MD轨迹。本文利用氯仿的隐式溶剂化模型,建立了刚性有机分子与聚γ-苄基-谷氨酸的结合自由能的计算方案和数值实现。模型参数拟合了在显式氯仿中由MD模拟得到的炼金术结合自由能,并与MD结果和另一个隐式溶剂化模型进行了比较。该方法的可能应用是基于物理上有意义的评分函数的对接或蒙特卡罗模拟,用于快速预测配体的相互作用姿势,以进行选择性结合或分析物的排列。
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来源期刊
CiteScore
5.80
自引率
9.10%
发文量
965
审稿时长
1.6 months
期刊介绍: An essential criterion for acceptance of research articles in the journal is that they provide new physical insight. Please refer to the New Physical Insights virtual issue on what constitutes new physical insight. Manuscripts that are essentially reporting data or applications of data are, in general, not suitable for publication in JPC B.
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