摘要
背景:SARS-CoV-2 (Mpro) 的主要蛋白酶是 SARS-CoV-2(COVID-19 的病原体)中确定的靶标之一。 X 射线衍射晶体学的应用使得该蛋白质靶标与配体复合的三维结构可用,这为对接研究铺平了道路。 目标:我们的目标是回顾最近在应用对接模拟以使用 AutoDock4 程序识别 Mpro 抑制剂方面所做的努力。 方法:我们搜索了 PubMed,以确定应用 AutoDock4 对接该蛋白质靶标的研究。我们使用 Mpro 可用的结构来分析分子间相互作用,并回顾了用于寻找抑制剂的方法。 结果:针对 Mpro 可用结构的对接应用发现配体具有在纳摩尔范围内的估计抑制。这种专注于晶体结构的计算方法揭示了 Mpro 的潜在抑制剂,可能对 SARS-CoV-2 表现出药理活性。然而,这些研究中的大多数都缺乏对对接协议的适当验证。此外,他们都忽略了机器学习在预测亲和力方面的潜在用途。 结论:结构数据与计算方法的结合为加速寻找治疗 COVID-19 的药物开辟了可能性。几项研究使用 AutoDock4 来寻找 Mpro 的抑制剂。他们中的大多数人没有采用经过验证的对接协议,这为对其计算方法的批评提供了支持。此外,其中一项研究报告了氯喹和羟氯喹与 Mpro 的结合。这项研究忽略了反对使用这些抗疟药治疗 COVID-19 的科学证据。
关键词: COVID-19、SARS-CoV-2、蛋白质-配体相互作用、autoDock4、对接、机器学习、主蛋白酶。
Current Medicinal Chemistry
Title:Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2
Volume: 28 Issue: 37
关键词: COVID-19、SARS-CoV-2、蛋白质-配体相互作用、autoDock4、对接、机器学习、主蛋白酶。
摘要:
Background: The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies.
Objective: Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4.
Methods: We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors.
Results: The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity.
Conclusion: The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.
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Cite this article as:
Protein-Ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2, Current Medicinal Chemistry 2021; 28 (37) . https://dx.doi.org/10.2174/0929867328666210329094111
DOI https://dx.doi.org/10.2174/0929867328666210329094111 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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