Abstract

The Burkholderia cepacia complex (Bcc) is a group of bacteria including several opportunistic human pathogens. Immunocompromised individuals and cystic fibrosis patients are especially vulnerable to serious infections by these bacteria, motivating the search for compounds with antimicrobial activity against the Bcc. The natural product ubonodin is a lasso peptide with promising activity against several Bcc species, working by inhibiting RNA polymerase in susceptible bacteria. In this study, we developed a high-throughput screen using next-generation sequencing to examine the fitness of a library of over 90,000 ubonodin variants, generating the most comprehensive dataset on lasso peptide activity so far. This screen revealed information regarding the structure-activity relationship of ubonodin over a large sequence space, indicating certain residues that can tolerate amino acid substitutions and still retain activity. Remarkably, the screen identified one variant with not only improved activity compared to wild-type ubonodin but also a sub-micromolar minimum inhibitory concentration (MIC) against a clinical isolate of the Bcc member Burkholderia cenocepacia. Ubonodin and several of the variants identified in this study had a lower MIC against certain Bcc strains than many clinically approved antibiotics. Finally, the large library size enabled us to develop DeepLasso, a deep learning model that can predict the RNAP inhibitory activity of an ubonodin variant.