Abstract
Protein-RNA interactions play a critical role in various biological processes and disease development. Proteins interact with RNAs through dynamic binding sites that exhibit specific structural patterns under various cellular conditions. While current computational methods take RNA structures in vivo into account, they fall short in capturing the structure contextual association of nucleotides, limiting predictive accuracy. Here, diPaRIS, a deep learning method, is proposed to predict dynamic protein-RNA interactions with improved accuracy and enhanced interpretability by integrating RNA structures in vivo. diPaRIS introduces a novel encoding scheme for SHAPE-seq to encode nucleotide correlations, providing a more comprehensive representation of RNA structures. Leveraging a U-shaped network architecture, diPaRIS not only improves prediction performance but also enables interpretable analysis by learning sequence binding motifs and generating attribution maps. Benchmarking diPaRIS across 44 datasets shows its superiority over existing methods. The model consistently achieves the highest accuracy, AUC, AUPR, and F1-score across all datasets. Additionally, diPaRIS excels in cross-cell line predictions, consistently outperforming the second-best method across all datasets. Predictions by diPaRIS reflect the conservation of protein-RNA binding and facilitate further functional interpretation of genetic variants in complex diseases. The findings highlight that diPaRIS effectively predicts protein-RNA interactions and interprets potential gene-disease associations.