Abstract

According to the world health organization (WHO) reports, Acinetobacter baumannii was considered as one of the significant and first-line priority pathogens, which causes hospital-acquired nosocomial infections in human. The enzymes involved in the peptidoglycan biosynthetic pathway are critical for the survival of this bacterium. Therefore, these enzymes are ideal drug target since they are conserved among most of the species and non-homologous to human. Here, we utilized the structure-based virtual screening (SBVS) technique to identify the promising lead molecules against MurB (UDP-N-acetylenolpyruvoylglucosamine reductase) protein using computational approaches. Initially, the three-dimensional structure of MurB was predicted based on MurB from P. aeruginosa (PDB ID: 4JAY), which is used as a structural template for homology modeling. During the High-throughput Virtual screening (HTVS) analysis, we started with 30,792 molecules against MurB model, among these; only 5238 molecules could be considered suitable for further step. Finally, only twenty molecules were able to pass Lipinski’s and ADMET properties. After a thorough examination of interaction analysis, higher ΔG and Ki values, we had chosen five promising molecules (ZINC IDs: ZINC12530134, ZINC15675540, ZINC15675762, ZINC15675624 and ZINC15707270) and three control molecules (PubChem IDs: 54682555, 729933 and 39964628) for Molecular dynamics (MD) simulation to understand the effect of ligands towards the structural stability, structural integrity and structural compactness of MurB protein. Further, the MM/PBSA binding free energy analysis was performed for eight ligands bound MurB structures. Together the results obtained from global dynamics, essential dynamics and MM-PBSA binding free energy analysis, we concluded that apart from the control molecules, ZINC12530134 should be considered as one of the most promising ones and it could be the potent inhibitor against A baumannii and provide valuable insight for further experimental studies.

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