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Multidrug-resistant Staphylococcus aureus represents a major clinical challenge because of the increasing prevalence of antimicrobial resistance and efflux-mediated resistance mechanisms. The MepR-MepA regulatory system is involved in the control of multidrug efflux in S. aureus, making MepR a relevant target for investigating compounds capable of modulating this regulatory system. In the present study, an integrated genomic and structure-based computational workflow was applied to S. aureus strain M48 to characterize antimicrobial resistance determinants and prioritize potential MepR-binding compounds. Whole-genome analysis using the Comprehensive Antibiotic Resistance Database (CARD) and Resistance Gene Identifier (RGI) identified the resistance-associated genetic repertoire of the strain, followed by comparative analysis of selected determinants. The MepR structure was evaluated using structural modelling and experimental structural information, and candidate binding regions were investigated through virtual screening and molecular docking. Adozelesin and zoliflodacin were prioritized based on their predicted interactions with MepR. Molecular-dynamics simulations were subsequently used to examine the structural behavior of the resulting complexes, while complementary physicochemical, pharmacokinetic, drug-likeness, and toxicity predictions were performed to characterize their computational ADMET profiles. Adozelesin showed a more favorable predicted docking score, whereas zoliflodacin displayed comparatively lower structural fluctuations and more persistent protein-ligand interactions during the simulated trajectory. These findings identify adozelesin and zoliflodacin as computationally prioritized MepR-binding candidates for further investigation. Importantly, the present study does not establish MepR inhibition or demonstrate functional effects on MepR-mediated repression or mepA expression. Experimental binding, DNA-binding, transcriptional, and antibacterial studies are required to determine the biological significance of the predicted interactions.
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Hussain et al. (2026) studied this question.
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