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使用同源建模、分子对接、分子动力学模拟和 MM-PBSA 结合自由能计算预测 CB1 和 CB2 配体的结合亲和力和选择性。

Prediction of the Binding Affinities and Selectivity for CB1 and CB2 Ligands Using Homology Modeling, Molecular Docking, Molecular Dynamics Simulations, and MM-PBSA Binding Free Energy Calculations.

机构信息

Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.

出版信息

ACS Chem Neurosci. 2020 Apr 15;11(8):1139-1158. doi: 10.1021/acschemneuro.9b00696. Epub 2020 Apr 2.

Abstract

Cannabinoids are a group of chemical compounds that have been used for thousands of years due to their psychoactive function and systemic physiological effects. There are at least two types of cannabinoid receptors, CB1 and CB2, which belong to the G protein-coupled receptor superfamily and can trigger different signaling pathways to exert their physiological functions. In this study, several representative agonists and antagonists of both CB1 and CB2 were systematically studied to predict their binding affinities and selectivity against both cannabinoid receptors using a set of hierarchical molecular modeling and simulation techniques, including homology modeling, molecular docking, molecular dynamics (MD) simulations and end point binding free energy calculations using the molecular mechanics/Poisson-Boltzmann surface area-WSAS (MM-PBSA-WSAS) method, and molecular mechanics/generalized Born surface area (MM-GBSA) free energy decomposition. Encouragingly, the calculated binding free energies correlated very well with the experimental values and the correlation coefficient square (), 0.60, was much higher than that of an efficient but less accurate docking scoring function ( = 0.37). The hotspot residues for CB1 and CB2 in both active and inactive conformations were identified MM-GBSA free energy decomposition analysis. The comparisons of binding free energies, ligand-receptor interaction patterns, and hotspot residues among the four systems, namely, agonist-bound CB1, agonist-bound CB2, antagonist-bound CB1, and antagonist-bound CB2, enabled us to investigate and identify distinct binding features of these four systems, with which one can rationally design potent, selective, and function-specific modulators for the cannabinoid receptors.

摘要

大麻素是一类化合物,由于其具有致幻作用和全身生理效应,已经被使用了数千年。至少有两种大麻素受体,CB1 和 CB2,它们属于 G 蛋白偶联受体超家族,可以触发不同的信号通路来发挥其生理功能。在这项研究中,我们系统地研究了 CB1 和 CB2 的几种代表性激动剂和拮抗剂,使用了一系列层次化的分子建模和模拟技术,包括同源建模、分子对接、分子动力学 (MD) 模拟和使用 MM-PBSA-WSAS 方法计算的终点结合自由能,以及 MM-GBSA 自由能分解,来预测它们对两种大麻素受体的结合亲和力和选择性。令人鼓舞的是,计算出的结合自由能与实验值非常吻合,相关系数平方()为 0.60,明显高于高效但不够准确的对接评分函数(=0.37)。通过 MM-GBSA 自由能分解分析,确定了 CB1 和 CB2 在活性和非活性构象中的热点残基。对四个系统(即激动剂结合的 CB1、激动剂结合的 CB2、拮抗剂结合的 CB1 和拮抗剂结合的 CB2)的结合自由能、配体-受体相互作用模式和热点残基进行比较,使我们能够研究和鉴定这四个系统的不同结合特征,从而可以合理设计针对大麻素受体的高效、选择性和功能特异性调节剂。

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