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<p>The emergence of multidrug-resistant (MDR) strains of <i>Salmonella typhimurium</i> (<i>S. typhimurium</i>) causes a significant global health challenge and underscores the need to develop potential antimicrobial agents. Here, we considered RamR, the major transcriptional repressor of the AcrAB-TolC efflux pump system, to identify promising inhibitors that can restore antibiotic susceptibility. We adopted an integrated computational-experimental research strategy that involved in silico screening of a structurally diverse compound database. The top four candidates (144095451, 17515455, 26648946, and 26648774) were selected for detailed analysis, which included re-docking, molecular dynamics (MD) simulations, binding free energy calculations, and free energy landscape analysis mapping. Density functional theory (DFT) was employed to explain the electronic properties and chemical reactivity of these molecules. To enhance the predictive accuracy of inhibitory potency (pIC₅₀), a machine learning (ML) regression model was developed, in which the ExtraTrees algorithm demonstrated high performance (<i>R</i><sup>2</sup> = 0.975). Among the top-ranked compounds, 144095451 emerged as the most promising RamR inhibitor, as indicated by both computational predictions and ML modelling. Experimental verification with isothermal titration calorimetry (ITC) confirmed strong binding affinity (Ka = 5.43 × 10⁶ M⁻<sup>1</sup>; Δ<i>H</i> = –53.18&#xa0;kcal/mol; stoichiometry <i>n</i> = 1.74) of 144095451. Antimicrobial profiling also established its efficacy, with a minimum inhibitory concentration (MIC) of 121.65 ± 0.5&#xa0;µg/mL and a zone of inhibition of 18.54 ± 0.76. These results highlight compound 144095451 as a promising RamR-targeted antimicrobial lead. This research highlights the potential of the combinatorial approach, which utilizes computational screening, structural dynamics, machine learning-based biological activity prediction, and experimental confirmation of candidate molecules against multidrug-resistant <i>S. typhimurium</i>.</p>

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Structure-guided discovery and validation of a potent RamR inhibitor targeting efflux-mediated multidrug resistance in Salmonella typhimurium

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • Gopal Prasad Agrawal,
  • Md Shamsher Alam,
  • Faisal Alotaibi,
  • Mohammad H. Alqarni,
  • Ahmed I. Foudah,
  • Aftab Alam,
  • Zafer Saad Al shehri,
  • Abdulaziz Saleh Alkhoshaiban,
  • Faez Falah Alshehri

摘要

The emergence of multidrug-resistant (MDR) strains of Salmonella typhimurium (S. typhimurium) causes a significant global health challenge and underscores the need to develop potential antimicrobial agents. Here, we considered RamR, the major transcriptional repressor of the AcrAB-TolC efflux pump system, to identify promising inhibitors that can restore antibiotic susceptibility. We adopted an integrated computational-experimental research strategy that involved in silico screening of a structurally diverse compound database. The top four candidates (144095451, 17515455, 26648946, and 26648774) were selected for detailed analysis, which included re-docking, molecular dynamics (MD) simulations, binding free energy calculations, and free energy landscape analysis mapping. Density functional theory (DFT) was employed to explain the electronic properties and chemical reactivity of these molecules. To enhance the predictive accuracy of inhibitory potency (pIC₅₀), a machine learning (ML) regression model was developed, in which the ExtraTrees algorithm demonstrated high performance (R2 = 0.975). Among the top-ranked compounds, 144095451 emerged as the most promising RamR inhibitor, as indicated by both computational predictions and ML modelling. Experimental verification with isothermal titration calorimetry (ITC) confirmed strong binding affinity (Ka = 5.43 × 10⁶ M⁻1; ΔH = –53.18 kcal/mol; stoichiometry n = 1.74) of 144095451. Antimicrobial profiling also established its efficacy, with a minimum inhibitory concentration (MIC) of 121.65 ± 0.5 µg/mL and a zone of inhibition of 18.54 ± 0.76. These results highlight compound 144095451 as a promising RamR-targeted antimicrobial lead. This research highlights the potential of the combinatorial approach, which utilizes computational screening, structural dynamics, machine learning-based biological activity prediction, and experimental confirmation of candidate molecules against multidrug-resistant S. typhimurium.