Morphological deconvolution of beta-lactam polyspecificity inE. coli

Beta-lactam antibiotics comprise one of the earliest known classes of antibiotic therapies. These molsecules covalently inhibit enzymes from the family of penicillin-binding proteins, which are essential to the construction of the bacterial cell wall. As a result, beta-lactams have long been known to cause striking changes to cellular morphology. The exact nature of the changes tend to vary by the precise PBPs engaged in the cell since beta-lactams exhibit a range of PBP enzyme specificity. The traditional method for exploring beta-lactam polyspecificity is a gel-based binding assay which is low-throughput and typically run ex situ in cell extracts. Here, we describe a medium-throughput, image-based assay combined with machine learning methods to automatically profile the activity of beta-lactams in E. coli cells. By testing for morphological change across a panel of strains with perturbations to individual PBP enzymes, our approach automatically and quantifiably relates different beta-lactam antibiotics according to their preferences for individual PBPs in cells. We show the potential of our approach for guiding the design of novel inhibitors towards different PBP-binding profiles by recapitulating the activity of two recently-reported PBP inhibitors.