AI Insight
Researchers developed COPAL, a computational pipeline that combines multiple protein-ligand co-folding models to predict which acyl-homoserine lactone (AHL) signaling molecules are preferred by LuxR-family quorum sensing receptors in bacteria. Testing on 96 experimentally characterized receptor-ligand pairs showed COPAL correctly placed the preferred AHL among the top 6 candidates (out of 58 possibilities) for 68% of receptors, outperforming individual models. The team applied COPAL to predict preferred ligands for approximately 10,000 LuxR receptors and experimentally validated predictions for a previously uncharacterized receptor from Mesorhizobium sp. NJ3.
Why it matters
This tool addresses a major gap in understanding bacterial quorum sensing systems, where most receptor-ligand relationships remain unknown despite extensive genomic data. By enabling prediction of signaling molecule specificity across thousands of bacterial species, COPAL could accelerate research into bacterial communication networks and potentially inform strategies to modulate bacterial collective behaviors in medical, agricultural, or environmental contexts.
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⚠️ Preprint – Noch nicht peer-reviewed
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Acyl-homoserine lactone (AHL) quorum sensing enables many species of proteobacteria to coordinate collective behaviors. In such systems, a synthase produces an AHL signal, which is sensed by a LuxR-family receptor. Despite extensive genomic annotation of LuxR homologs, preferred AHLs for most receptors remain unknown, limiting functional understanding of quorum sensing across diverse bacteria. Here, we present the COPAL (combining ordered predictions of audited ligands) pipeline, which integrates multiple protein-ligand co-folding models to identify preferred AHLs for a specific LuxR. Benchmarking on a leakage-controlled subset of 96 experimentally characterized LuxR-AHL pairs shows that COPAL places the preferred AHL within the top-6 candidates (out of 58) for 68% of receptors, outperforming every individual co-folding model. Further, inter-model agreement correlates with ranking accuracy, offering an indication of confidence. We show that COPAL resolves the specificity shift induced by three-point mutations in LasR and correctly nominates C8-HSL as the preferred ligand for the previously uncharacterized Mesorhizobium sp. NJ3 receptor, which we verified experimentally. Finally, we release the Ranked AHL-LuxR Prediction Hub (RALPH), comprising precomputed rankings for about 10,000 unique LuxR homologs. More broadly, COPAL shows that unweighted rank aggregation of complementary co-folding models offers a general strategy for predicting receptor-ligand specificity in data-scarce biological systems.