Predictive Identification and Design of Potent Inhibitors Targeting Resistance-inducing Candidate Genes from Whole-genome Sequences

This work utilizes predictive modeling in drug discovery to unravel potential candidate genes from that are implicated in antimicrobial resistance; we subsequently target the gidB, MacB, and KatG genes with some compounds from plants with reported antibacterial potentials. The resistance genes and plasmids were identified from 10 whole-genome sequence datasets of ; forty two plant compounds were selected, and their 3D structures were retrieved and optimized for docking. The 3D crystal structures of KatG, MacB, and gidB were retrieved and prepared for molecular docking, molecular dynamics simulations, and ADMET profiling. Hesperidin showed the least binding energy (kcal/mol) against KatG (-9.3), MacB (-10.7), and gidB (-6.7); additionally, good pharmacokinetic profiles and structure-dynamics integrity with their respective protein complexes were observed. Although these findings suggest hesperidin as a potential inhibitor against MacB, gidB, and KatG in , further validations through and experiments are needed. This research is expected to provide an alternative avenue for addressing existing antimicrobial resistances associated with 's MacB, gidB, and KatG.