Molecular Determinants of Functional Bacterial sRNA–mRNA Interactions Revealed by Integrating RNA Interactomes and Interpretable Machine Learning

The molecular features that determine the functional outcome of sRNA-mRNA interactions remain poorly defined. Here, we integrated Hfq-CLASH interactome mapping with matched transcriptomic and proteomic profiling in Escherichia coli and developed an interpretable machine-learning framework to identify the molecular determinants that distinguish functional from non-functional interactions.

Using sequence, structural, thermodynamic, duplex and protein-occupancy features, we found that transcriptomic and proteomic responses could be predicted with above-chance performance, achieving AUCs of 0.78 and 0.74, respectively. Feature attribution revealed that physical pairing alone is insufficient for regulation; instead, regulatory outcome is shaped by a coordinated interplay between RNA secondary structure, thermodynamic accessibility and local protein-binding context.