Molecular Determinants of Functional Bacterial sRNA–mRNA Interactions Revealed by Integrating RNA Interactomes and Interpretable Machine Learning
Bacterial small RNAs (sRNAs) regulate gene expression by base pairing with target mRNAs, yet transcriptome-wide interactome mapping has shown that many sRNA–mRNA interactions detected in vivo have modest or no regulatory effect using orthogonal reporter assays. The features that determine functional outcome 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 determinants that distinguish functional from non-functional interactions. Using sequence, structural, thermodynamic, duplex and protein-occupancy features, transcriptomic and proteomic responses were 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. Target-side Hfq occupancy emerged as a positive predictor of functional regulation, whereas AR2-domain occupancy on the sRNA was associated with non-responsive interactions, suggesting that distinct ribonucleoprotein states may separate productive regulation from non-productive binding. These findings indicate that the regulatory fate of an sRNA–mRNA interaction is an emergent property of its biophysical context and protein-binding environment, rather than a direct consequence of physical pairing alone. GRAPHICAL ABSTRACT