Ghahremanian Shabnam, Rashidi Mohammad Mehdi, Raeisi Kimai, Toghraie Davood
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, PR China.
Faculty of Mechanical and Industrial Engineering, Quchan University of Technology, Quchan, Iran.
J Mol Liq. 2022 May 15;354:118901. doi: 10.1016/j.molliq.2022.118901. Epub 2022 Mar 9.
Since the commencement of the novel Coronavirus, the disease has quickly turned into a worldwide crisis so that there has been growing attention in discovering possible hit compounds for tackling this pandemic. Discovering standard treatment strategies is a serious challenge because little information is available about this emerged infectious virus. Regarding the high impact of time, applying computational procedures to choose promising drugs from a catalog of licensed medications provides a precious chance for combat against the life-threatening disorder of COVID-19. Molecular dynamics (MD) simulation is a promising approach for assessing the binding affinity of ligand-receptor as well as observing the conformational trajectory of docked complexes over time. Given that many computational studies are performed using MD along with the molecular docking on various candidates as antiviral inhibitors of COVID-19 protease, there is a demand to conduct a comprehensive review of the most important studies to reveal and compare the potential introduced agents that this study covers this defect. In this context, the present review intends to prepare an overview of these studies by considering RMSD, RMSF, radius of gyration, binding free energy, and Solvent-Accessible Surface Area (SASA) as effective parameters for evaluation. The outcomes will offer a road map for adjusting antiviral inhibitors, which can facilitate the selection and development of drug candidates for use in the medical therapy. Finally, the molecular modeling approaches rendered by this study may be valuable for future computational studies.
自新型冠状病毒出现以来，该疾病迅速演变成一场全球危机，因此人们越来越关注寻找可能有效的化合物来应对这一疫情。由于关于这种新出现的传染性病毒的信息很少，寻找标准的治疗策略是一项严峻的挑战。鉴于时间的紧迫性，应用计算程序从已获许可的药物目录中筛选有前景的药物，为抗击危及生命的COVID-19疾病提供了宝贵的机会。分子动力学（MD）模拟是一种很有前途的方法，可用于评估配体-受体的结合亲和力以及观察对接复合物随时间的构象轨迹。鉴于许多计算研究使用MD以及分子对接对各种候选物进行研究，以寻找作为COVID-19蛋白酶抗病毒抑制剂的药物，因此需要对最重要的研究进行全面综述，以揭示和比较本研究涵盖的潜在引入药物。在此背景下，本综述旨在通过将均方根偏差（RMSD）、均方根波动（RMSF）、回转半径、结合自由能和溶剂可及表面积（SASA）作为有效评估参数，对这些研究进行概述。研究结果将为调整抗病毒抑制剂提供路线图，这有助于选择和开发用于医学治疗的候选药物。最后，本研究提出的分子建模方法可能对未来的计算研究具有重要价值。
