Division of Infectious Diseases and Division of Computer-Aided Drug Design, The Red-Green Research Centre, BICCB, Tejgaon, Dhaka, Bangladesh.
Key Laboratory of Soft Chemistry and Functional Materials of MOE, Department of Chemical Engineering, Nanjing University of Science and Technology, Nanjing, China.
J Biomol Struct Dyn. 2021 Oct;39(16):6231-6241. doi: 10.1080/07391102.2020.1794974. Epub 2020 Jul 21.
Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.
计算机辅助药物筛选通过分子对接、分子动力学（MD）和结构活性关系（SAR），为识别有希望的 COVID-19 治疗药物再利用候选药物提供了一种有效方法。在这项研究中，通过将 1615 种美国食品和药物管理局（FDA）批准的药物与 SARS-CoV-2 的主要蛋白酶（Mpro）进行分子对接来进行计算筛选。基于结合能、拟合分数和受体结合部位的非共价相互作用，几种有前途的已批准药物，包括西咪普韦、麦角胺、溴隐亭和他达拉非，脱颖而出，成为最佳候选药物。所有选定的药物都与关键活性位点残基相互作用，包括 His41 和 Cys145。各种非共价相互作用，包括氢键、疏水相互作用、pi-硫和 pi-pi 相互作用，似乎在药物-Mpro 复合物中占主导地位。对最有前途的药物进行 MD 模拟。在药物-Mpro 复合物中观察到结构稳定性和紧凑性。在 MD 模拟过程中，蛋白质在 apo 和 holo 形式下都表现出低灵活性。还测量了所选药物的 MM/PBSA 结合自由能。为了进行模式识别、结构相似性和结合能预测，使用多元线性回归（MLR）模型进行定量结构活性关系。MLR 模型预测的结合能与分子对接确定的结合能具有 82%的准确性。我们的详细结果可以促进针对 SARS-CoV-2 主要蛋白酶的合理药物设计。由 Ramaswamy H. Sarma 交流。
