Predictive identification and design of potent inhibitors targeting resistance-inducing candidate genes from E. coli whole-genome sequences - Aborode, Abdullahi Tunde; Kumar, Neeraj; Olowosoke, Christopher Busayo; Ibisanmi, Tope Abraham; Ayoade, Islamiyyah; Umar, Haruna Isiyaku; Jamiu, Abdullahi Temitope; Bolarinwa, Basit; Olapade, Zainab; Idowu, Abidemi Ruth; Adelakun, Ibrahim O.; Onifade, Isreal Ayobami; et al. (Frontiers in Bioinformatics, 2024)
The work aims to utilize predictive modelling in drug discovery to unravel potential candidate genes from E. coli that are implicated in antimicrobial resistance and subsequently target gidB, MacB and KatG with some plant compounds from plants with reported antibacterial potentials. The resistance genes and plasmid were identified from Ten whole-genome sequence data sets of E. coli, forty-two plant compounds were selected with their 3D structures retrieved and optimized for docking, 3D crystals 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). good pharmacokinetics profile and structure-dynamics integrity with their respective protein complexes was observed. While these findings suggest hesperidin as a potential inhibitor against MacB, gidB and KatG in E. coli, validation through urgent in vitro and in vivo experiments is important going forward. This research holds promise for providing an alternative avenue to address existing antimicrobial resistance associated with E. coli's MacB, gidB, and KatG.