bioRxivBiochemistryPreprint
Small molecule inhibitors of the NorA multidrug efflux pump potentiate antibiotic activity by binding the outward-open conformation
Janine L. GrayiD, Elizabeth V. K. LedgeriD, Tiffany SuwattheeiD, Thomas J. BurdeniD, Konstantina Arvaniti, Amber Y. Sefton, Lydia E. PapagoraiD, Thomas B. ClarkeiD, Jennifer Riley, Érika G. PintoiD, Fraser CunninghamiD, Ian Hugh GilbertiD, David W. GrayiD, Da‐Neng WangiD, Kevin D. ReadiD, Thomas Lanyon‐HoggiD, Nathaniel J. TraasethiD, Andrew M. EdwardsiD, Edward W. TateiD
Abstract
Antibiotic resistance is among the greatest threats of the modern era. Multidrug efflux pumps expel antibiotics from bacterial cells and present a particular challenge by conferring resistance to a broad range of antibiotic classes; however, there is currently a lack of potent and selective inhibitors. Here, we report the discovery of IMP-2380, a drug-like chemical probe for the multidrug efflux pump NorA that delivers low-nanomolar potentiation of ciprofloxacin activity in vitro and activity in an in vivo S. aureus infection model. A phenotypic high-throughput screen for inhibitors of the ciprofloxacin-activated SOS DNA repair pathway in methicillin-resistant Staphylococcus aureus (MRSA) identified hit compounds targeting NorA, and subsequent optimization established IMP-2380 as the most potent NorA inhibitor discovered to date. The structure of NorA bound to IMP-2380 was solved by cryo-electron microscopy at 2.52 [A] resolution, revealing that the small molecule locks the pump in the outward-open conformation. This closes the inner face and prevents antibiotics binding from the cytosol, providing an explanation for the exceptional potency of IMP-2380 and structure-activity relationship across the series. IMP-2380 represents an in vivo active NorA inhibitor, functioning via a structurally defined outward-open binding mode, and will enable future exploration of NorA as a druggable target to combat antibiotic resistance.
- organism: human
- assay: structural biology
- research focus: drug response / drug discovery
- approach: experimental assay
- approach: deep learning
- approach: statistical model
- Citations
- 0
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