Identification of novel multitarget antitubercular inhibitors against mycobacterial peptidoglycan biosynthetic Mur enzymes by structure-based virtual screening.

Current therapeutic strategies for several diseases, including infection, have evolved from an initial single-target treatment to a multitarget one. A multitarget antitubercular drugs targeting different mycobacterial proteins are more effective at suppressing bacterial growth. In this study, a high throughput virtual screening was performed to identify hits to the potential antitubercular multitarget: murA, murB, murC, murD, murE, murF, murG and murI from that is involved in peptidoglycan biosynthesis. In the virtual screening, we were docked 56,400 compounds of the ChEMBL antimycobacterial library and re-scored and identified the top 10 ranked compounds as antitubercular drug candidates. Further, the best common docked complex CHEMBL446262 was subjected to molecular dynamics simulation to understand the molecule's stability in the presence of an active site environment. After that, we have calculated binding free energy the top-ranked docked complexes using the MM/PBSA method. These ligands exhibited the highest binding affinity; find out novel drug-likeness might show the effect's inhibitor by interacting with multitarget Mur enzymes. New antitubercular therapies that include multitarget drugs may have higher efficacy than single-target medicines and provide a more straightforward antitubercular therapy regimen.Communicated by Ramaswamy H. Sarma.