Duong Cuong Quoc, Nguyen Phuong Thuy Viet
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam.
ACS Omega. 2023 Feb 6;8(7):6679-6688. doi: 10.1021/acsomega.2c07259. eCollection 2023 Feb 21.
With the emergence of antibody-evasive omicron subvariants (BA.2.12.1, BA.4, and BA.5), which can compromise the efficacy of vaccination, it is of utmost importance to widen the finite therapeutic options for COVID-19. Although more than 600 co-crystal complexes of Mpro with inhibitors have been revealed, utilizing them to search for novel Mpro inhibitors remains limited. Although there were two major groups of Mpro inhibitors, covalent and noncovalent inhibitors, noncovalent inhibitors were our main focus due to the safety concerns with their covalent counterparts. Hence, this study aimed to explore Mpro noncovalent inhibition ability of phytochemicals extracted from Vietnamese herbals by combining multiple structure-based approaches. By closely inspecting 223 complexes of Mpro with noncovalent inhibitors, a 3D-pharmacophore model representing typical chemical features of Mpro noncovalent inhibitors was generated with good validation scores (sensitivity = 92.11%, specificity = 90.42%, accuracy = 90.65%, and goodness-of-hit score = 0.61). Afterward, the pharmacophore model was applied to explore the potential Mpro inhibitors from our in-house Vietnamese phytochemical database, revealing 18 substances, 5 of which were assayed. The remaining 13 substances were then examined by induced-fit molecular docking, revealing 12 suitable compounds. A machine-learning activity prediction model was developed to rank the hit, suggesting nigracin and calycosin-7--β-glucopyranoside as promising Mpro natural noncovalent inhibitors.
随着具有抗体逃避能力的奥密克戎亚变体(BA.2.12.1、BA.4和BA.5)的出现,它们会削弱疫苗接种的效果,因此拓宽针对新冠病毒病有限的治疗选择至关重要。尽管已揭示了600多种M蛋白酶与抑制剂的共晶复合物,但利用它们来寻找新型M蛋白酶抑制剂仍然有限。虽然有两大类M蛋白酶抑制剂,即共价抑制剂和非共价抑制剂,但由于共价抑制剂的安全性问题,非共价抑制剂是我们的主要关注点。因此,本研究旨在通过结合多种基于结构的方法,探索从越南草药中提取的植物化学物质对M蛋白酶的非共价抑制能力。通过仔细检查223个M蛋白酶与非共价抑制剂的复合物,生成了一个代表M蛋白酶非共价抑制剂典型化学特征的三维药效团模型,其验证分数良好(灵敏度=92.11%,特异性=90.42%,准确度=90.65%,命中优值分数=0.61)。随后,将该药效团模型应用于从我们内部的越南植物化学数据库中探索潜在的M蛋白酶抑制剂,发现了18种物质,其中5种进行了测定。然后通过诱导契合分子对接对其余13种物质进行了检查,发现了12种合适的化合物。开发了一种机器学习活性预测模型对命中结果进行排名,表明黑曲霉素和毛蕊异黄酮-7-O-β-D-吡喃葡萄糖苷是有前景的M蛋白酶天然非共价抑制剂。