Authors
Williams, S. C., Greenstein, G., Liu, X., Fragasso, A., Eckman, N., Prossnitz, A., Dong, C., Makar-Limanov, A., Jacobs-Wagner, C., Cegelski, L., Lopez Hernandez, H., Appel, E.
Abstract
Antimicrobial resistance poses an urgent and increasing threat to global health. The development of new antimicrobials is crucial. Synthetic copolymers are attractive as a potential solution, because they can be produced at scale and designed to mimic antimicrobial peptides and act as broad-spectrum antimicrobials capable of evading resistance mechanisms. This work leverages a cross-molecular machine learning pipeline, trained on antimicrobial peptides, to develop potent antimicrobial polymers to combat Escherichia coli, which were then synthesized and validated experimentally. One candidate copolymer was further characterized and shown to permeabilize the bacterial membrane, which is associated with decreased resistance. Furthermore, this copolymer demonstrated remarkable synergy in eradicating biofilm-associated E. coli when combined with a first-line clinical drug regimen, reducing the amount needed to eradicate bacteria in biofilms by three orders of magnitude. These results demonstrate promise for potentiating antibacterial activity of currently available antibiotics, treating serious and complicated infections, and combatting antimicrobial resistance.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Nov 2025.
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