Integrative In Vitro and In Silico Analysis of Origanum majorana Flavonoids: Targeting β-Lactamase and Penicillin-Binding Proteins Through Conserved Region-Based Structural Optimization
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Abstract
Growing bacterial pathogens with antibiotic resistance necessitates novel therapeutic strategies, raising the exploration and exploitation of the potential of plant natural products as alternative drugs. This study investigated the antibacterial potential of flavonoids from Origanum majorana L. through a combined in vitro and in silico approach, focusing on their inhibitory effects against β-lactamase and penicillin-binding protein. The in vitro antibacterial activities of O. majorana extracts, Apigenin (a flavonoid), and imipenem were evaluated against bacterial isolates through inhibition zone assays, and the analyses of minimal inhibitory concentration and minimal bactericidal concentration. In silico molecular docking was performed to elucidate the inhibition mechanism and assess binding affinities and interactions between flavonoids and target proteins. The docking results revealed that flavonoids exhibited stronger binding affinities with both target proteins (-9.5 kcal/mol and -5.8 kcal/mol) compared to imipenem, particularly against β-lactamases. The flavonoids enhanced the binding and stability of protein-ligand complexes through key molecular interactions, increasing the inhibitory effects on proteins. Our findings highlighted the potential of O. majorana flavonoids as promising resistance-modifying agents against β-lactam-resistant bacterial infections. Notably, a genomic analysis-based conserved region prediction approach was employed to construct a high-quality, structurally optimized model of the target proteins, enabling superior binding interactions with flavonoids. This structural refinement strategy enhances the predictive accuracy of computational drug design and may facilitate the development of novel antibacterial agents with improved specificity. Continued exploration of conserved region-based protein modeling can significantly enhance the rational design of next-generation inhibitors with improved binding affinity and therapeutic efficacy.
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