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利用分子动力学(MD)模拟和分层虚拟筛选从天然来源中对具有药代动力学特性的新型抗SARS-CoV-2主要蛋白酶(M)分子进行计算机模拟鉴定。

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (M) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening.

作者信息

Onyango Harrison, Odhiambo Patrick, Angwenyi David, Okoth Patrick

机构信息

Department of Biological Sciences (Molecular Biology, Computational Biology and Bioinformatics Section), School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.

Department of Mathematics, School of Natural and Applied Sciences, Masinde Muliro University of Science and Technology, P. O BOX 190, Kakamega 50100, Kenya.

出版信息

J Trop Med. 2022 Oct 10;2022:3697498. doi: 10.1155/2022/3697498. eCollection 2022.

Abstract

Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 M. Compounds such as cinanserin, nelfinavir, baicalin, baicalein, candesartan cilexetil, chloroquine, dipyridamole, and hydroxychloroquine have the ability to prevent SARS-CoV-2 M from facilitating COVID 19 infection; thus, they treat COVID 19. However, these drugs majorly act to reduce the symptoms of the disease. No anti-viral drug against COVID 19 virus infection has been discovered and approved. Therefore, this study sought to explore natural inhibitors of SARS-CoV-2 M to develop a pharmacophore model for virtual screening of natural compounds in the ZINC database as potential candidates for SARS-CoV-2 M inhibitors and as therapeutic molecules against COVID 19. This study undertook in silico methods to identify the best anti-viral candidates targeting SAR-CoV-2 M from natural sources in the ZINC database. Initially, reported anti-SARS-CoV-2 M molecules were integrated into designing a pharmacophore model utilizing PharmaGist. Later, the pharmacophore model was loaded into ZINCPHARMER and screened against the ZINC database to identify new probable drug candidates. The root means square deviation (RMSD) values of the potential drug candidates informed the selection of some of them, which were docked with SARS-CoV-2 M to comprehend their interactions. From the molecular docking results, the top four candidates (, , , and ) against SARS-CoV-2 M, with binding energies ranging from -8.2 kcal/mol to -8.6 kcal/mol, were examined for their oral bioavailability and other pharmacokinetic properties. Consequently, emerged as the only orally bioavailable drug candidate with desirable pharmacokinetic properties. This candidate drug was used to perform MD simulations, and the outcomes revealed that formed a stable complex with the viral main protease. Consequently, emerges as a potential anti-SARS-CoV-2 M inhibitor for the production of new COVID 19 drugs.

摘要

近年来,诸如严重急性呼吸综合征冠状病毒(SARS-CoV)、中东呼吸综合征冠状病毒(MERS-CoV)和严重急性呼吸综合征冠状病毒2(SARS-CoV-2)等感染源引发了高死亡率的疫情。对抗此类病原体需要快速开发新型治疗化合物。不幸的是,传统的药物开发方法既耗时又昂贵。在本研究中,利用计算算法对ZINC数据库中的天然化合物库进行虚拟筛选,以确定它们对SARS-CoV-2 M的亲和力。辛硫乙胺、奈非那韦、黄芩苷、黄芩素、坎地沙坦酯、氯喹、双嘧达莫和羟氯喹等化合物有能力阻止SARS-CoV-2 M促成新型冠状病毒肺炎(COVID-19)感染;因此,它们可治疗COVID-19。然而,这些药物主要作用是减轻疾病症状。尚未发现并批准针对COVID-19病毒感染的抗病毒药物。因此,本研究试图探索SARS-CoV-2 M的天然抑制剂,以开发一种药效团模型,用于虚拟筛选ZINC数据库中的天然化合物,作为SARS-CoV-2 M抑制剂的潜在候选物以及针对COVID-19的治疗分子。本研究采用计算机方法从ZINC数据库中的天然来源识别靶向SARS-CoV-2 M的最佳抗病毒候选物。最初,将已报道的抗SARS-CoV-2 M分子整合到利用PharmaGist设计的药效团模型中。随后,将药效团模型加载到ZINCPHARMER中,并针对ZINC数据库进行筛选,以识别新的可能的药物候选物。潜在药物候选物的均方根偏差(RMSD)值为其中一些候选物的选择提供了依据,这些候选物与SARS-CoV-2 M进行对接以了解它们的相互作用。根据分子对接结果,对针对SARS-CoV-2 M的前四名候选物(、、和)进行了口服生物利用度和其他药代动力学性质的研究,其结合能范围为-8.2千卡/摩尔至-8.6千卡/摩尔。结果,成为唯一具有理想药代动力学性质的口服生物利用度药物候选物。该候选药物用于进行分子动力学(MD)模拟,结果表明与病毒主要蛋白酶形成了稳定的复合物。因此,成为生产新型COVID-19药物的潜在抗SARS-CoV-2 M抑制剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d68/9576439/17dbf09335b4/JTM2022-3697498.001.jpg

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