Discovery and artificial intelligence-guided mechanistic elucidation of a narrow-spectrum antibiotic

ABSTRACT Current clinical antibiotics are largely broad-spectrum agents that can alter the gut microbiome and promote colonization by Enterobacteriaceae, which are often drug resistant. This includes adherent-invasive Escherichia coli (AIEC), particularly in patients with inflammatory bowel disease, in which dysbiosis creates a niche for this pathogen to colonize. There is an urgent and unmet need for novel narrow-spectrum and microbiome-sparing antibiotics. Here we screened 10,747 bioactive small molecules for antibacterial activity against AIEC and discovered enterololin, an antibacterial compound with targeted activity against Enterobacteriaceae species. Enterololin could overcome intrinsic and acquired resistance mechanisms in clinical isolates when combined with a subinhibitory concentration of SPR741, a polymyxin B analogue used here to increase outer membrane permeability in Gram-negative bacteria. Molecular substructure- and deep learning-guided mechanism-of-action investigations revealed that enterololin perturbs lipoprotein trafficking through a mechanism involving the LolCDE complex, laboratory-evolved resistant mutants predominantly mapped to lolC and lolE, with an in vitro frequency of resistance of ~10−8 to 10−7. Enterololin showed low mammalian cytotoxicity (HEK293 half-maximal inhibitory concentration ~100 µg ml−1) and suppressed AIEC infection in mouse models when administered in combination with SPR741, while largely preserving the overall microbiome composition. This study highlights the utility of deep learning methods for predicting molecular interactions and identifies a promising Enterobacteriaceae-specific antibacterial candidate for further development.

Co-authors: Denise B. Catacutan, Vian Tran, Autumn Arnold, Jeremie Alexander, Gabriele Corso, Yeganeh Yousefi, Megan M. Tu, Stewart McLellan, Dominique Tertigas, Jakob Magolan, Michael G. Surette, Eric D. Brown, Brian K. Coombes