Identification of novel compounds against Acinetobacter baumannii 3-oxoacyl-[acyl-carrier-protein] synthase I (FabB) via comprehensive structure-based computational approaches
Journal of Molecular Graphics and Modelling, ISSN: 1093-3263, Vol: 124, Page: 108565
2023
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Article Description
Acinetobacter baumannii is one of the most serious opportunistic pathogens according to WHO. The difference between bacterial and mammalian fatty acid biosynthesis pathways makes FASII enzymes attractive targets in drug discovery. 3-oxoacyl-[acyl-carrier-protein] synthase I (FabB) from the FAS II pathway catalyze the condensation of malonyl ACP with acyl-ACP, and elongates the fatty acid chain by two carbons. To investigate potential inhibitors of the A. baumannii FabB, we used computational approaches including homology modeling, high-throughput virtual screening, molecular docking, molecular dynamics simulations, and MM-GBSA free energy calculations. After the high-throughput virtual screening, the resulting ligands were further screened using the QM-polarized ligand docking (QPLD) and induced fit docking (IFD) approaches. Molecular dynamics simulations were performed for 100 ns. And according to binding free energy calculations, we have identified nine compounds with the best binding affinities. Three of these compounds were selected for an additional 1 μs MD simulation to assess ligand stability. Two of them named L6 and L7 showed promised stability and affinity to the target. Here, we present novel compounds against A. baumannii FabB via structure-based computational approaches. These compounds might pave the way for the design of new lead structures and inhibitors for multidrug-resistant A. baumannii.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1093326323001638; http://dx.doi.org/10.1016/j.jmgm.2023.108565; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85165231728&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/37454410; https://linkinghub.elsevier.com/retrieve/pii/S1093326323001638; https://dx.doi.org/10.1016/j.jmgm.2023.108565
Elsevier BV
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