Abstract
The global rise in antibiotic resistance necessitates new agents targeting essential bacterial processes like protein synthesis. Structure-based virtual screening enables the rapid identification of drug candidates from large chemical libraries, accelerating drug discovery. Here, we report an integrated computational and experimental pipeline to identify small-molecule translation inhibitors targeting the catalytic cavity of the E. coli ribosome. A consensus docking strategy using Glide and AutoDock Vina, combined with pharmacophore filtering and interaction analysis, was applied to FDA-approved, experimental, and investigational drug libraries to prioritize candidate compounds. The binding free energies of the compounds were estimated using restrained molecular dynamics (MD) simulations coupled with the MM-GBSA method, where the computational efficiency was improved by truncating the ribosome-ligand complexes. Guided by these results and our previous work on the E. coli 30S decoding center, 14 hit compounds were selected for the in vitro antibacterial and translation inhibition assays. Among these, Mitoxantrone (IC(50) = 14.10 ± 0.38 µM) was identified as a translation inhibitor with a bacteriostatic effect comparable to the antibiotic Clindamycin. Whereas Plerixafor (IC(50) = 62.30 ± 6.47 µM), Olcegepant (IC(50) = 144.30 ± 16.41 µM), and Ziritaxestat (IC(50) = 224.30 ± 25.02 µM) showed inhibitory effects at higher concentrations. Notably, Mitoxantrone has the potential to be an anticancer agent and a translation inhibitor that may significantly benefit cancer patients by addressing secondary bacterial infections. The pharmacokinetic and toxicological profiles of these compounds are already well-characterized. Overall, this work illustrates a useful drug discovery strategy combining virtual screening, MD simulations, and experimental validation to identify ribosome-targeting inhibitors and can be extended to other challenging RNA targets and protein-RNA complexes.