Goodarzi Narjes Noori, Shahbazi Behzad, Khiavi Elham Haj Agha Gholizadeh, Asforooshani Mahshid Khazani, Abed Sahar, Badmasti Farzad
Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran.
Curr Comput Aided Drug Des. 2025;21(5):708-720. doi: 10.2174/0115734099297360240312043642.
Drug-resistant 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 Materials and Methods: 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 CHNO 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 CHNO are promising inhibitors of FemA in . To further validate these computational results, experimental studies are planned to confirm the inhibitory effects of these compounds on various strains. Combining computational screening with experimental validation contributes valuable insights to the field of drug discovery in comparison to the classical drug discovery approaches.
耐药性是全球范围内一项重大的医疗挑战,其可用的治疗选择范围在持续逐渐减少。因此,本研究旨在鉴定针对FemA的潜在抑制剂,FemA是参与[具体细菌名称]细胞壁生物合成的一种关键蛋白。材料与方法:筛选过程包括在StreptomDB数据库上基于结构进行全面的虚拟筛选,以使用AutoDock Vina鉴定对FemA具有潜在抑制作用的配体。选择具有最高结合亲和力和药代动力学性质的最理想配体。使用GROMACS 2018模拟软件包通过分子动力学(MD)进一步分析具有最多氢键和疏水相互作用的两种配体。
选择了六个氢供体保守残基作为蛋白质活性位点,包括Arg-220、Tyr-38、Gln-154、Asn-73、Arg-74和Thr-24。通过虚拟筛选,共鉴定出九种与FemA蛋白具有最高结合亲和力的化合物。弗氏环素和CHNO表现出最高的结合亲和力,并显示出良好的药代动力学性质。FemA-配体复合物的分子动力学分析进一步表明复合物具有理想的稳定性和可靠性,增强了这些配体作为FemA蛋白抑制剂的潜在效力。
我们的研究结果表明,弗氏环素和CHNO是[具体细菌名称]中FemA的有前景的抑制剂。为了进一步验证这些计算结果,计划进行实验研究以确认这些化合物对各种[具体细菌名称]菌株的抑制作用。与传统药物发现方法相比,将计算筛选与实验验证相结合为药物发现领域提供了有价值的见解。