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基于结构的虚拟筛选对糖皮质激素受体进行药物重定位,提出替代调节剂。

Drug repositioning to propose alternative modulators for glucocorticoid receptor through structure-based virtual screening.

机构信息

Graduate Program of Computational Biology and Bioinformatics, Graduate School of Science and Engineering, Kadir Has University, Istanbul, Turkey.

Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey.

出版信息

J Biomol Struct Dyn. 2022;40(21):11418-11433. doi: 10.1080/07391102.2021.1960608. Epub 2021 Aug 6.

Abstract

Drug repositioning has recently become one of the widely used drug design approaches in proposing alternative compounds with potentially fewer side effects. In this study, structure-based pharmacophore modelling and docking was used to screen existing drug molecules to bring forward potential modulators for ligand-binding domain of human glucocorticoid receptor (hGR). There exist several drug molecules targeting hGR, yet their apparent side effects still persist. Our goal was to disclose new compounds via screening existing drug compounds to bring forward fast and explicit solutions. The so-called pharmacophore model was created using the most persistent pharmacophore features shared by several crystal structures of the receptor. The model was first used to screen a small database of 75 agonists and 300 antagonists/decoys, and exhibited a successful outcome in its ability to distinguish agonists from antagonists/decoys. Then, it was used to screen a database of over 5000 molecules composed of FDA-approved, worldwide used and investigational drug compounds. A total of 110 compounds satisfying the pharmacophore requirements were subjected to different docking experiments for further assessment of their binding ability. In the final hit list of 54 compounds which fulfilled all scoring criteria, 19 of them were nonsteroidal and when further investigated, each presented a unique scaffold with little structural resemblance to any known nonsteroidal GR modulators. Independent 100 ns long MD simulations conducted on three selected drug candidates in complex with hGR displayed stable conformations incorporating several hydrogen bonds common to all three compounds and the reference molecule dexamethasone.Communicated by Ramaswamy H. Sarma.

摘要

药物重定位最近已成为广泛使用的药物设计方法之一,旨在提出潜在副作用更少的替代化合物。在这项研究中,我们使用基于结构的药效团建模和对接技术筛选现有的药物分子,以寻找人类糖皮质激素受体(hGR)配体结合域的潜在调节剂。目前已有几种针对 hGR 的药物分子,但它们明显的副作用仍然存在。我们的目标是通过筛选现有的药物化合物来揭示新的化合物,从而快速、明确地解决问题。所谓的药效团模型是使用受体的几个晶体结构中共享的最持久的药效团特征创建的。该模型首先用于筛选一个包含 75 种激动剂和 300 种拮抗剂/诱饵的小数据库,其区分激动剂和拮抗剂/诱饵的能力取得了成功。然后,我们用它来筛选一个由 5000 多种药物化合物组成的数据库,这些化合物包括 FDA 批准的、全球使用的和正在研究的药物化合物。共有 110 种满足药效团要求的化合物进行了不同的对接实验,以进一步评估它们的结合能力。在满足所有评分标准的 54 种化合物的最终命中列表中,有 19 种是非甾体化合物,进一步研究发现,它们每个都具有独特的支架,与任何已知的非甾体 GR 调节剂结构相似性很小。在与 hGR 复合物中对三个选定的候选药物进行的独立的 100 ns 长 MD 模拟显示,它们都有稳定的构象,包含了所有三种化合物和参考分子地塞米松共有的几个氢键。

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