aceDiscovery of dietary polyphenols targeting the autoinducer-2 quorum sensing system of pathogenic bacteria
Targeting autoinducer-2 (AI-2) quorum sensing (QS) systems with dietary compounds represents a promising strategy to combat pathogens, yet mechanisms remain elusive. Here, we develop a machine learning-driven framework combining computational screening with multi-level experimental analysis to identify AI-2 quorum sensing interference molecules (QSIMs). A graph neural network (GNN)-based classifier (AI2IMpred) screens over 7000 phytochemicals and identifies dietary polyphenols as potent QSIMs. Using Salmonella T…
A machine learning classifier screening over 7000 phytochemicals identified 17 polyphenols that bind quorum sensing proteins in Salmonella, pointing toward diet-based options against drug-resistant infections.
The work centers on a Salmonella Typhimurium model with laboratory and cell-based assays, and the authors frame the results only as insights for potential development of interventions.
Evidence
- Peer-reviewedGut Microbes2026-10-03
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Truvace Impact Record TRV-2026-1348, v1: “Discovery of dietary polyphenols targeting the autoinducer-2 quorum sensing system of pathogenic bacteria.” Truvace, 2026-10-14. /record/TRV-2026-1348 (accessed at citation time). sha256 4e09292a19595438…
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