虚拟筛选
药物重新定位
金黄色葡萄球菌
药物发现
药品
化学
计算生物学
配体(生物化学)
自动停靠
生物信息学
生物化学
药理学
组合化学
生物
受体
遗传学
细菌
基因
作者
Narjes Noori Goodarzi,Behzad Shahbazi,Elham Haj Agha Gholizadeh Khiavi,Mahshid Khazani Asforooshani,Saif Mazeel Abed,Farzad Badmasti
出处
期刊:Current Computer - Aided Drug Design
[Bentham Science]
日期:2024-03-20
卷期号:20
标识
DOI:10.2174/0115734099297360240312043642
摘要
Drug-resistant Staphylococcus aureus represents a substantial healthcare challenge worldwide, and its range of available therapeutic options continues to diminish progressively. Thus, this study aimed to identify potential inhibitors against FemA, a crucial protein involved in the cell wall biosynthesis of S. aureus.The screening process involved a comprehensive structure-based virtual screening on the StreptomDB database to identify ligands with potential inhibitory effects on FemA using AutoDock Vina. The most desirable ligands with the highest binding affinity and pharmacokinetic properties were selected. Two ligands with the highest number of hydrogen bonds and hydrophobic interactions were further analyzed by molecular dynamics (MD) using the GROMACS version 2018 simulation package.Six H-donor conserved residues were selected as protein active sites, including Arg- 220, Tyr-38, Gln-154, Asn-73, Arg-74, and Thr-24. Through virtual screening, a total of nine compounds with the highest binding affinity to the FemA protein were identified. Frigocyclinone and C21H21N3O4 exhibited the highest binding affinity and demonstrated favorable pharmacokinetic properties. Molecular dynamics analysis of the FemA-ligand complexes further indicated desirable stability and reliability of complexes, reinforcing the potential efficacy of these ligands as inhibitors of FemA protein.Our findings suggest that Frigocyclinone and C21H21N3O4 are promising inhibitors of FemA in S. aureus. To further validate these computational results, experimental studies are planned to confirm the inhibitory effects of these compounds on various S. aureus strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.
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