Integrated machine learning, molecular dynamics, and density functional theory approaches for identifying potential inhibitor targeting TEM 𝛽-lactamase in Escherichia coli.

Enzyme-mediated antimicrobial resistance via β-lactamases, including TEM-1 and TEM-235 in Escherichia coli, significantly undermines the therapeutic efficacy of β-lactam antibiotics. β-lactamases are primary drivers of resistance against β-lactam drugs and remain critical targets for inhibitor development. In this study, 3576 antibacterial compounds were evaluated for drug-like pharmacokinetic properties, of which 55 compounds that met drug-likeness and non-toxicity criteria were shortlisted for virtual screening against TEM β-lactamase. Molecular docking analysis showed that compound 344,265 exhibited high binding affinities with both TEM-1 (-8.5 kcal/mol) and TEM-235 (-8.4 kcal/mol), which are primarily determined by the H-bonding and multiple intermolecular interactions. Stable protein-ligand interactions were demonstrated using molecular dynamics simulations and binding energy calculation using molecular mechanics/Poisson-Boltzmann surface area method. The density functional theory analysis showed that the compound had a moderate HOMO-LUMO energy gap, indicating chemical stability. Also, the surface mapping of the molecular electrostatic potential revealed important reactive sites that could be used to achieve favorable binding orientations. These results demonstrate compound 344,265 as a promising lead molecule targeting the TEM enzyme. Still, it needs further experimental validation to confirm its inhibitory activity and translate these in silico findings into therapeutic applications.