药效团
虚拟筛选
对接(动物)
计算生物学
药物发现
蛋白酵素
蛋白酶
化学
小分子
分子动力学
分子力学
生物
生物化学
酶
医学
计算化学
护理部
作者
Mohanad A. Mahgoub,Ahmed Alnaem,Mohammed Fadlelmola,Mazin Abo-idris,Alaa A. Makki,Abdelgadir A. Abdelgadir,Abdulrahim A. Alzain
标识
DOI:10.1080/07391102.2022.2112080
摘要
The pandemic of coronavirus disease is caused by the SARS-CoV-2 which is considered a global health issue. The main protease of COVID 19 (Mpro) has an important role in viral multiplication in the host cell. Inhibiting Mpro is a novel approach to drug discovery and development. Also, transmembrane serine proteases (TMPSS2) facilitate viral activation by cleavage S glycoproteins, thus considered one of the essential host factors for COVID-19 pathogenicity. Computational tools were widely used to reduce time and costs in search of effective inhibitors. A chemical library that contains over two million molecules was virtually screened against TMPRSS2. Also, XP docking for the top hits was screened against (Mpro) to identify dual-target inhibitors. Furthermore, MM-GBSA and predictive ADMET were performed. The top hits were further studied through density functional theory (DFT) calculation and showed good binding to the active sites. Moreover, molecular dynamics (MD) for the top hits were performed which gave information about the stability of the protein-ligand complex during the simulation period. This study has led to the discovery of potential dual-target inhibitors Z751959696, Z751954014, and Z56784282 for COVID-19 with acceptable pharmacokinetic properties. The outcome of this study can participate in the development of novel inhibitors to defeat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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