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利用综合药物再利用策略发现有效的新冠病毒主蛋白酶抑制剂。

Discovery of potent Covid-19 main protease inhibitors using integrated drug-repurposing strategy.

机构信息

Department of Biotechnology, School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, India.

出版信息

Biotechnol Appl Biochem. 2021 Aug;68(4):712-725. doi: 10.1002/bab.2159. Epub 2021 Apr 14.

Abstract

The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the significant role of the main protease of Covid-19 for virus replication, we performed a drug-repurposing study using the recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR, respectively, were developed using available small molecule inhibitors and utilized in the screening of the DrugBank repository. Further, a hierarchical docking protocol was implemented with the support of the Glide algorithm. The resultant compounds were then examined for their binding free energy against the main protease of Covid-19 by means of the Prime-MM/GBSA algorithm. Most importantly, the machine learning-based AutoQSAR algorithm was used to predict the antiviral activities of resultant compounds. The hit molecules were also examined for their drug-likeness and toxicity parameters through the QikProp algorithm. Finally, the hit compounds activity against the main protease was validated using molecular dynamics simulation studies. Overall, the present analysis yielded two potential inhibitors (DB02986 and DB08573) that are predicted to bind with the main protease of Covid-19 better than currently used drug molecules such as N3 (cocrystallized native ligand), lopinavir, and ritonavir.

摘要

新型严重急性呼吸系统综合症冠状病毒 2(SARS-CoV-2)在全球范围内的出现和迅速传播,对公共卫生构成了紧迫威胁。急需新型抗病毒疗法来克服这一大流行。鉴于新冠病毒主要蛋白酶对于病毒复制的重要作用,我们使用最近存储的主要蛋白酶结构 6LU7 进行了药物再利用研究。例如,使用现有的小分子抑制剂分别开发了基于药效团和电子药效团的假说,如 AARRH 和 AARR,并将其用于药物库的筛选。此外,还实施了基于分层对接协议的 Glide 算法支持。然后,使用基于 Prime-MM/GBSA 算法的方法,通过计算所得化合物与新冠病毒主要蛋白酶的结合自由能来检查它们的结合能力。最重要的是,使用基于机器学习的 AutoQSAR 算法来预测所得化合物的抗病毒活性。通过 QikProp 算法还检查了命中分子的药物相似性和毒性参数。最后,使用分子动力学模拟研究验证了命中化合物对主要蛋白酶的活性。总体而言,该分析产生了两种潜在的抑制剂(DB02986 和 DB08573),它们被预测比目前使用的药物分子(如 N3(共结晶的天然配体)、洛匹那韦和利托那韦)更好地与新冠病毒的主要蛋白酶结合。

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