Discovery and AI-guided mechanistic elucidation of a novel Enterobacteriaceae-specific antibiotic

Current clinical antibiotics are largely broad-spectrum agents that promote intestinal dysbiosis and colonisation of Enterobacteriaceae, which are often drug-resistant. Indeed, dysbiosis creates an ideal niche for adherent-invasive Escherichia coli (AIEC) in patients with inflammatory bowel disease (IBD). There is an urgent and unmet need for novel narrow-spectrum and microbiome-sparing antibiotics. Here, we screened >10,000 molecules for antibacterial activity against AIEC and discovered enterololin, an antibacterial compound with targeted activity against Enterobacteriaceae species. Molecular substructure- and deep learning-guided mechanism of action investigations revealed that enterololin perturbs lipoprotein trafficking through a mechanism involving the LolCDE complex. Moreover, enterololin can suppress an AIEC infection in mouse models, while largely preserving the overall microbiome composition. This work highlights the utility of deep learning methods for predicting molecular interactions, thereby accelerating mechanism of action elucidation of novel molecules, and identifies a promising Enterobacteriaceae-specific antibacterial candidate for further development to treat challenging infections in IBD patients. Overall design: RNA-seq profiling of E. coli BW25113 ?bamB?tolC (DKO) cells diluted 1:10,000 into 50 ml fresh LB and grown to mid-log (~107 CFU ml-1) at 37°C with shaking at 200 RPM, at which time cultures were supplemented with 5× MIC enterololin or no drug and grown for an additional 2 h or 4 h