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Increasing resistance to common antibiotics is becoming a major challenge that requires the development of new antibacterial agents. Peptidoglycan is an essential heteropolymer of the bacterial envelope that ensures the integrity and shape of all bacteria and is also an important target for antibiotics. The biosynthesis of peptidoglycan depends on a lipid carrier, undecaprenyl phosphate. As a byproduct of peptidoglycan polymerization, the lipid carrier is released as undecaprenyl pyrophosphate, which must be recycled to allow new polymerization cycles. To this end, it undergoes a dephosphorylation process catalyzed by the membrane phosphatase BacA, which is specific and highly conserved in bacteria. In the present study, we identified small molecules displaying inhibitory potency towards BacA. We began by preparing a commercial compound library, followed by high-throughput virtual screening by ensemble docking using the 3D structure of BacA and molecular dynamics snapshots to account for the flexibility of the protein. Of 83 compounds computationally selected and tested in a biochemical assay, one sulfamoylthiophene molecule showed significant inhibition of the undecaprenyl pyrophosphate dephosphorylation activity catalyzed by BacA. Subsequently, an additional 33 scaffold analogs were selected and acquired, of which 6 compounds exhibited BacA inhibition. The IC50 values of these compounds ranged from 42 to 366 μM. In addition, significant antibacterial activity against Escherichia coli was observed in TolC/PAP2-depleted strains. We believe that the overall strategy followed in this study and the identified class of inhibitors provide a solid foundation for the further development of potent BacA-targeted inhibitors and the discovery of novel antibacterial compounds. © 2022 The Author(s).

Citation

Marko Jukič, Rodolphe Auger, Victor Folcher, Matic Proj, Hélène Barreteau, Stanislav Gobec, Thierry Touzé. Towards discovery of inhibitors of the undecaprenyl-pyrophosphate phosphatase BacA by virtual high-throughput screening. Computational and structural biotechnology journal. 2022;20:2360-2371


PMID: 35664230

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