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通过基于药效基团的虚拟筛选、分子对接和 MD 模拟方法鉴定针对 SARS-CoV-2 的 RdRp 抑制剂。

Identification of RdRp inhibitors against SARS-CoV-2 through E-pharmacophore-based virtual screening, molecular docking and MD simulations approaches.

机构信息

School of Biochemistry and Biotechnology, University of the Punjab, Lahore, Punjab, Pakistan; Department of Human Genetics and Molecular Biology, University of Health Sciences, Lahore, Punjab, Pakistan.

School of Biological Sciences, University of the Punjab, Quaid e Azam Campus, Lahore, Punjab, Pakistan.

出版信息

Int J Biol Macromol. 2023 May 15;237:124169. doi: 10.1016/j.ijbiomac.2023.124169. Epub 2023 Mar 28.

Abstract

The outbreak of novel Coronavirus, an enduring pandemic declared by WHO, has consequences to an alarming ongoing public health menace which has already claimed several million human lives. In addition to numerous vaccinations and medications for mild to moderate COVID-19 infection, lack of promising medication or therapeutic pharmaceuticals remains a serious concern to counter the ongoing coronavirus infections and to hinder its dreadful spread. Global health emergencies have called for urgency for potential drug discovery and time is the biggest constraint apart from the financial and human resources required for the high throughput drug screening. However, computational screening or in-silico approaches appeared to be an effective and faster approach to discover potential molecules without sacrificing the model animals. Accumulated shreds of evidence on computational studies against viral diseases have revealed significance of in-silico drug discovery approaches especially in the time of urgency. The central role of RdRp in SARS-CoV-2 replication makes it promising drug target to curtain on going infection and its spread. The present study aimed to employ E-pharmacophore-based virtual screening to reveal potent inhibitors of RdRp as potential leads to block the viral replication. An energy-optimised pharmacophore model was generated to screen the Enamine REAL DataBase (RDB). Then, ADME/T profiles were determined to validate the pharmacokinetics and pharmacodynamics properties of the hit compounds. Moreover, High Throughput Virtual Screening (HTVS) and molecular docking (SP & XP) were employed to screen the top hits from pharmacophore-based virtual screening and ADME/T screen. The binding free energies of the top hits were calculated by conducting MM-GBSA analysis followed by MD simulations to determine the stability of molecular interactions between top hits and RdRp protein. These virtual investigations revealed six compounds having binding free energies of -57.498, -45.776, -46.248, -35.67, -25.15 and -24.90 kcal/mol respectively as calculated by the MM-GBSA method. The MD simulation studies confirmed the stability of protein ligand complexes, hence, indicating as potent RdRp inhibitors and are promising candidate drugs to be further validated and translated into clinics in future.

摘要

新型冠状病毒的爆发,被世界卫生组织宣布为持久的大流行,对持续的公共卫生威胁造成了惊人的后果,已经夺走了数百万人的生命。除了为数众多的针对轻度至中度 COVID-19 感染的疫苗和药物外,缺乏有前途的药物或治疗药物仍然是对抗持续的冠状病毒感染并阻止其可怕传播的严重关切。全球卫生紧急情况要求迫切需要发现潜在药物,除了高通量药物筛选所需的财务和人力资源外,时间是最大的限制。然而,计算筛选或计算机模拟方法似乎是一种有效的、更快的方法,可以在不牺牲模型动物的情况下发现潜在分子。针对病毒疾病的计算研究积累的大量证据表明,计算机药物发现方法特别是在紧急情况下具有重要意义。RdRp 在 SARS-CoV-2 复制中的核心作用使其成为阻止正在进行的感染和传播的有前途的药物靶点。本研究旨在采用基于 E-药效团的虚拟筛选来揭示 RdRp 的有效抑制剂,作为阻止病毒复制的潜在先导化合物。生成了一个能量优化的药效团模型,以筛选 Enamine REAL 数据库 (RDB)。然后,确定 ADME/T 特性以验证命中化合物的药代动力学和药效学特性。此外,还采用高通量虚拟筛选 (HTVS) 和分子对接 (SP 和 XP) 筛选基于药效团的虚拟筛选和 ADME/T 筛选的顶级命中。通过进行 MM-GBSA 分析并进行 MD 模拟,计算顶级命中与 RdRp 蛋白之间分子相互作用的结合自由能,计算顶级命中的结合自由能。这些虚拟研究表明,根据 MM-GBSA 方法计算,六种化合物的结合自由能分别为-57.498、-45.776、-46.248、-35.67、-25.15 和-24.90 kcal/mol。MD 模拟研究证实了蛋白质配体复合物的稳定性,因此,这些化合物被认为是有效的 RdRp 抑制剂,是有前途的候选药物,可在未来进一步验证并转化为临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/19fe/10043960/9fcadd503d98/gr1_lrg.jpg

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