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基于配体/基于结构的虚拟筛选、分子动力学模拟和结合能计算发现 SARS-CoV-2 主要蛋白酶的有效抑制剂。

Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations.

机构信息

Chemistry Department, Memorial University, St. John's, NL A1B 3X7, Canada.

出版信息

Phys Chem Chem Phys. 2020 Oct 21;22(40):23099-23106. doi: 10.1039/d0cp04326e.

Abstract

COVID-19 has caused lockdowns all over the world in early 2020, as a global pandemic. Both theoretical and experimental efforts are seeking to find an effective treatment to suppress the virus. In silico drug design can play a vital role in identifying promising drug candidates against COVID-19. Herein, we focused on the main protease of SARS-CoV-2 that has crucial biological functions in the virus. We performed a ligand-based virtual screening followed by a docking screening for testing approved drugs and bioactive compounds listed in the DrugBank and ChEMBL databases. The top 8 docking results were advanced to all-atom MD simulations to study the relative stability of the protein-ligand interactions. MD simulations support that the catalytic residue, His41, has a neutral side chain with a protonated delta position. An absolute binding energy (ΔG) of -42 kJ mol-1 for the protein-ligand (Mpro-N3) complex has been calculated using the potential-of-mean-force (geometrical) approach. Furthermore, the relative binding energies were computed for the top docking results. Our results suggest several promising approved and bioactive inhibitors of SARS-CoV-2 Mpro as follows: a bioactive compound, ChEMBL275592, which has the best MM/GBSA binding energy; the second-best compound, montelukast, is an approved drug used in the treatment of asthma and allergic rhinitis; the third-best compound, ChEMBL288347, is a bioactive compound. Bromocriptine and saquinavir are other approved drugs that also demonstrate stability in the active site of Mpro, albeit their relative binding energies are low compared to the N3 inhibitor. This study provides useful insights into de novo protein design and novel inhibitor development, which could reduce the cost and time required for the discovery of a potent drug to combat SARS-CoV-2.

摘要

2020 年初,COVID-19 作为一种全球大流行病,导致全球各地都实行了封锁。理论和实验都在努力寻找有效的治疗方法来抑制这种病毒。计算机辅助药物设计可以在识别针对 COVID-19 的有前途的药物候选物方面发挥重要作用。在这里,我们重点研究了 SARS-CoV-2 的主要蛋白酶,它在病毒中具有至关重要的生物学功能。我们进行了基于配体的虚拟筛选,然后进行对接筛选,以测试 DrugBank 和 ChEMBL 数据库中列出的已批准药物和生物活性化合物。将前 8 个对接结果推进到全原子 MD 模拟中,以研究蛋白质-配体相互作用的相对稳定性。MD 模拟支持催化残基 His41 具有中性侧链和带质子的δ位置。使用平均力势能(几何)方法计算出蛋白质-配体(Mpro-N3)复合物的绝对结合能(ΔG)为-42 kJ mol-1。此外,还计算了前 8 个对接结果的相对结合能。我们的结果表明,Mpro 是几种有前途的 SARS-CoV-2 抑制剂,包括以下几种:一种生物活性化合物 ChEMBL275592,其 MM/GBSA 结合能最佳;第二种化合物孟鲁司特是一种用于治疗哮喘和过敏性鼻炎的已批准药物;第三种化合物 ChEMBL288347 是一种生物活性化合物。溴隐亭和沙奎那韦是其他已批准的药物,它们在 Mpro 的活性位点也表现出稳定性,尽管它们的相对结合能与 N3 抑制剂相比较低。这项研究为从头蛋白质设计和新型抑制剂开发提供了有用的见解,这可以降低发现有效对抗 SARS-CoV-2 的药物所需的成本和时间。

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