Division of Computer Aided Drug Design, The Red-Green Research Centre, BICCB, Dhaka, Bangladesh.
Department of Chemistry and Biochemistry, South Dakota State University, Brookings, SD, USA.
J Biomol Struct Dyn. 2021 Jun;39(9):3213-3224. doi: 10.1080/07391102.2020.1761883. Epub 2020 May 12.
The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
新型冠状病毒 2 的主要蛋白酶是设计和开发抗病毒药物的重要靶点之一。在这项研究中，我们选择了 40 种抗病毒植物化学物质，以找出最有潜力的候选物质，作为针对主要蛋白酶的有效抑制剂。使用 AutoDock Vina 和 GOLD 套件进行分子对接，以确定植物化学物质与主要蛋白酶之间的结合亲和力和相互作用。选定的候选物与关键的 Cys145 和 His41 残基强烈相互作用。为了验证对接相互作用，对包括金丝桃素、矢车菊素 3-葡萄糖苷、黄芩苷、甘草素和 α-酮酰胺-11r 在内的 5 种排名靠前的抑制剂进行了 100ns 的分子动力学（MD）模拟。MD 模拟的主成分分析（PCA）表明，黄芩苷、矢车菊素 3-葡萄糖苷和 α-酮酰胺-11r 与主蛋白酶的无配体形式具有结构相似性。这些发现也得到了均方根偏差（RMSD）、均方根波动（RMSF）、回转半径（Rg）和溶剂可及表面积（SASA）研究的强烈支持。PCA 还用于发现用于最佳配体模式识别的定量构效关系（QSAR）。QSAR 的多元线性回归（MLR）显示，训练集的 值为 0.842，测试集的 值为 0.753。我们提出的 MLR 模型可以预测有利的结合能，与分子对接检测到的结合能相比。ADMET 分析表明，这些候选物似乎是更安全的抑制剂。我们的综合计算和统计分析表明，这些选定的植物化学物质可用作针对新型冠状病毒的潜在抑制剂。由 Ramaswamy H. Sarma 交流。
