Department of Physical Sciences, University of Arkansas Fort Smith, 5210 Grand Avenue, Fort Smith, Arkansas 72904, United States.
Department of Chemistry, Shiv Nadar University, Gautam Buddha Nagar, Uttar Pradesh 201314, India.
J Chem Inf Model. 2021 Nov 22;61(11):5469-5483. doi: 10.1021/acs.jcim.1c00524. Epub 2021 Oct 20.
COVID-19, an acute viral pneumonia, has emerged as a devastating pandemic. Drug repurposing allows researchers to find different indications of FDA-approved or investigational drugs. In this current study, a sequence of pharmacophore and molecular modeling-based screening against COVID-19 M (PDB: 6LU7) suggested a subset of drugs, from the Drug Bank database, which may have antiviral activity. A total of 44 out of 8823 of the most promising virtual hits from the Drug Bank were subjected to molecular dynamics simulation experiments to explore the strength of their interactions with the SARS-CoV-2 M active site. MD findings point toward three drugs (DB04020, DB12411, and DB11779) with very low relative free energies for SARS-CoV-2 M with interactions at His41 and Met49. MD simulations identified an additional interaction with Glu166, which enhanced the binding affinity significantly. Therefore, Glu166 could be an interesting target for structure-based drug design. Quantitative structural-activity relationship analysis was performed on the 44 most promising hits from molecular docking-based virtual screening. Partial least square regression accurately predicted the values of independent drug candidates' binding energy with impressively high accuracy. Finally, the EC and CC of 10 drug candidates were measured against SARS-CoV-2 in cell culture. Nilotinib and bemcentinib had EC values of 2.6 and 1.1 μM, respectively. In summary, the results of our computer-aided drug design provide a roadmap for rational drug design of M inhibitors and the discovery of certified medications as COVID-19 antiviral therapeutics.
新型冠状病毒肺炎(COVID-19)是一种急性病毒性肺炎,已成为一种毁灭性的大流行病。药物再利用使研究人员能够发现已获美国食品和药物管理局(FDA)批准或正在研究的药物的不同适应症。在目前的这项研究中,针对 COVID-19 M(PDB:6LU7)的药效团和基于分子建模的筛选序列提示了来自 Drug Bank 数据库的一组药物可能具有抗病毒活性。从 Drug Bank 数据库中,共有 44 种最有希望的虚拟命中物进行了分子动力学模拟实验,以探索它们与 SARS-CoV-2 M 活性部位相互作用的强度。MD 研究结果表明,有三种药物(DB04020、DB12411 和 DB11779)与 SARS-CoV-2 M 的相互作用位于 His41 和 Met49,其相对自由能非常低。MD 模拟还确定了与 Glu166 的另一种相互作用,这显著增强了结合亲和力。因此,Glu166 可能是基于结构的药物设计的一个有趣靶点。对基于分子对接的虚拟筛选的 44 种最有希望的命中物进行了定量构效关系分析。偏最小二乘回归准确地预测了独立药物候选物的结合能值,具有令人印象深刻的高精度。最后,在细胞培养中测量了 10 种候选药物对 SARS-CoV-2 的 EC 和 CC 值。尼罗替尼和 bemcentinib 的 EC 值分别为 2.6 和 1.1 μM。总之,我们的计算机辅助药物设计结果为 M 抑制剂的合理药物设计和发现经过认证的药物作为 COVID-19 抗病毒疗法提供了路线图。