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环糊精主客体复合物的大规模亲和力计算:理解重组在分子识别过程中的作用。

Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process.

作者信息

Wickstrom Lauren, He Peng, Gallicchio Emilio, Levy Ronald M

机构信息

BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854 ; Department of Chemistry, Lehman College, The City University of New York, Bronx, NY 10468.

BioMaPS Institute for Quantitative Biology and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854.

出版信息

J Chem Theory Comput. 2013 Jul 9;9(7):3136-3150. doi: 10.1021/ct400003r.

Abstract

Host-guest inclusion complexes are useful models for understanding the structural and energetic aspects of molecular recognition. Due to their small size relative to much larger protein-ligand complexes, converged results can be obtained rapidly for these systems thus offering the opportunity to more reliably study fundamental aspects of the thermodynamics of binding. In this work, we have performed a large scale binding affinity survey of 57 β-cyclodextrin (CD) host guest systems using the binding energy distribution analysis method (BEDAM) with implicit solvation (OPLS-AA/AGBNP2). Converged estimates of the standard binding free energies are obtained for these systems by employing techniques such as parallel Hamitionian replica exchange molecular dynamics, conformational reservoirs and multistate free energy estimators. Good agreement with experimental measurements is obtained in terms of both numerical accuracy and affinity rankings. Overall, average effective binding energies reproduce affinity rank ordering better than the calculated binding affinities, even though calculated binding free energies, which account for effects such as conformational strain and entropy loss upon binding, provide lower root mean square errors when compared to measurements. Interestingly, we find that binding free energies are superior rank order predictors for a large subset containing the most flexible guests. The results indicate that, while challenging, accurate modeling of reorganization effects can lead to ligand design models of superior predictive power for rank ordering relative to models based only on ligand-receptor interaction energies.

摘要

主客体包合物是理解分子识别的结构和能量方面的有用模型。由于它们相对于大得多的蛋白质 - 配体复合物尺寸较小,因此可以快速获得这些系统的收敛结果,从而提供了更可靠地研究结合热力学基本方面的机会。在这项工作中,我们使用具有隐式溶剂化(OPLS - AA/AGBNP2)的结合能分布分析方法(BEDAM)对57个β - 环糊精(CD)主客体系统进行了大规模的结合亲和力调查。通过采用诸如并行哈密顿复制交换分子动力学、构象库和多态自由能估计器等技术,获得了这些系统的标准结合自由能的收敛估计值。在数值准确性和亲和力排名方面都与实验测量结果取得了良好的一致性。总体而言,平均有效结合能比计算得到的结合亲和力更好地再现了亲和力排序,尽管考虑了诸如结合时的构象应变和熵损失等效应的计算结合自由能与测量值相比具有更低的均方根误差。有趣的是,我们发现结合自由能对于包含最灵活客体的大子集是更好的排序预测指标。结果表明,虽然具有挑战性,但准确模拟重组效应可以导致相对于仅基于配体 - 受体相互作用能的模型具有更高预测能力的配体设计排序模型。

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