The permeability barrier of Gram-negative cell envelopes is the major obstacle in the discovery and development of new antibiotics. In Gram-negative bacteria, these difficulties are exacerbated by the synergistic interaction between two biochemically distinct phenomena, the low permeability of the outer membrane (OM) and active multidrug efflux. In this study, we used Pseudomonas aeruginosa and Escherichia coli strains with controllable permeability barriers, achieved through hyperporination of the OMs and varied efflux capacities, to evaluate the contributions of each of the barriers to protection from antibacterials. We analyzed antibacterial activities of β-lactams and fluoroquinolones, antibiotics that are optimized for targets in the periplasm and the cytoplasm, respectively, and performed a machine learning-based analysis to identify physicochemical descriptors that best classify their relative potencies. Our results show that the molecular properties selected by active efflux and the OM barriers are different for the two species. Antibiotic activity in P. aeruginosa was better classified by electrostatic and surface area properties, whereas topology, physical properties, and atom or bond counts best capture the behavior in E. coli. In several cases, descriptor values that correspond to active antibiotics also correspond to significant barrier effects, highlighting the synergy between the two barriers where optimizing for one barrier promotes strengthening of the other barrier. Thus, both barriers should be considered when optimizing antibiotics for favorable OM permeability, efflux evasion, or both.
Keywords: Gram-negative bacteria; antibiotic permeation; machine learning; multidrug efflux; outer membrane; physicochemical properties.