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通过有效的定制基于药效团的虚拟筛选、分子动力学模拟和结合自由能分析来识别阿尔茨海默病的 hQC 抑制剂。

Identifying hQC Inhibitors of Alzheimer's Disease by Effective Customized Pharmacophore-Based Virtual Screening, Molecular Dynamic Simulation, and Binding Free Energy Analysis.

机构信息

Department of Physical Chemistry, College of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, 510006, China.

Department of Cardiothoracic Surgery, Affiliated Second Hospital of Guangzhou Medical University, Guangzhou, 510260, China.

出版信息

Appl Biochem Biotechnol. 2019 Apr;187(4):1173-1192. doi: 10.1007/s12010-018-2780-9. Epub 2018 Sep 5.

DOI:10.1007/s12010-018-2780-9
PMID:30187344
Abstract

Human glutaminyl cyclase (hQC) appeared as a promising new target with its inhibitors attracted much attention for the treatment of Alzheimer's disease (AD) in recent years. But so far, only a few compounds have been reported as hQC inhibitors. To find novel and potent hQC inhibitors, a high-specificity ZBG (zinc-binding groups)-based pharmacophore model comprising customized ZBG feature was first generated using HipHop algorithm in Discovery Studio software for screening out hQC inhibitors from the SPECS database. After purification by docking studies and drug-like ADMET properties filters, four potential hit compounds were retrieved. Subsequently, these hit compounds were subjected to 30-ns molecular dynamic (MD) simulations to explore their binding modes at the active side of hQC. MD simulations demonstrated that these hit compounds formed a chelating interaction with the zinc ion, which was consistent with the finding that the electrostatic interaction was the major driving force for binding to hQC confirmed with MMPBSA energy decomposition. Higher binding affinities of these compounds were also verified by the binding free energy calculations comparing with the references. Thus, these identified compounds might be potential hQC candidates and could be used for further investigation.

摘要

近年来,人类谷氨酰胺环化酶(hQC)作为一种有前途的新靶点,其抑制剂受到了广泛关注,可用于治疗阿尔茨海默病(AD)。但迄今为止,仅报道了少数几种化合物作为 hQC 抑制剂。为了寻找新型有效的 hQC 抑制剂,首先使用 Discovery Studio 软件中的 HipHop 算法生成了一个基于高特异性 ZBG(锌结合基团)的药效团模型,该模型包含定制的 ZBG 特征,用于从 SPECS 数据库中筛选 hQC 抑制剂。经过对接研究和类药性 ADMET 属性筛选的纯化后,检索到了四个潜在的命中化合物。随后,对这些命中化合物进行了 30ns 的分子动力学(MD)模拟,以探索它们在 hQC 活性部位的结合模式。MD 模拟表明,这些命中化合物与锌离子形成了螯合相互作用,这与静电相互作用是与 hQC 结合的主要驱动力的发现一致,这一点也得到了 MMPBSA 能量分解验证。与参考文献相比,这些化合物的结合自由能计算也验证了它们具有更高的结合亲和力。因此,这些鉴定出的化合物可能是潜在的 hQC 候选物,可用于进一步研究。

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