Abstract
Abstract Protein–RNA interactions are fundamental to numerous cellular processes, yet quantitatively characterizing their binding specificity remains a major challenge. We present IRIS (Integrative RNA–protein interaction prediction Informed by Structure and sequence), a biophysical framework that integrates residue-level sequence and structural features without relying on large-scale affinity data to predict binding affinities and identify binding motifs. Applied across different protein–RNA systems, IRIS predicts relative binding free energies (ΔΔ G ) with consistent correlations and competitive error metrics, and its performance is further improved by incorporating additional high-affinity sequences into the training set. By leveraging predicted structural complexes, IRIS reveals alternative binding modes not observed in experimental structures, extends applicability to systems lacking experimental protein–RNA complexes, and generates a library of favorable RNA-binding motifs at protein–RNA interfaces. Collectively, these results establish IRIS as a versatile framework that leverages increasingly accurate structural predictions to enable quantitative modeling and rational engineering of protein–RNA interactions.