A Unified Structure-Based Deep-Learning Framework for High-Throughput Screening of Protein-Binding RNAs

Abstract

Protein-RNA interactions regulate diverse biological processes and are increasingly exploited in therapeutic RNA discovery, but accurate inferences of nucleotide preferences and reliable structure prediction remain challenging. Here, we present PRIS, a unified structure-based deep-learning framework that combines two complementary components: PRISeq for nucleotide probability estimation at each RNA position and PRIScore for residue-nucleotide distance prediction to discriminate native-like from incorrect poses. Both share a feature extractor that integrates an Anti-Symmetric Graph Attention Network (A-GAT) with sparse k-Maximum Inner Product (k-MPI) attention to capture long-range interactions across large graphs. PRIScore improves the selection of native-like protein-RNA predictions generated by AlphaFold3, achieving a top-1 success rate of 81.91% on a docking benchmark, compared to 79.26% for AlphaFold3. The selected structures are then fed into PRISeq, which infers position-specific binding preferences and screens RNA libraries. On a PWM benchmark, PRISeq achieved a mean absolute error (MAE) of 0.75, outperforming FoldX, Rosetta-based scoring functions, and NA-MPNN. In virtual screening against MS2 protein, PRISeq screens 129,248 RNA hairpins within 11.95 seconds, achieving the highest EF0.5% of 14.40, approximately double the best baseline. PRIS also effectively enriches active aptamers against NELF-E and GFP while preserving sequence diversity. By integrating structure selection with binding-preference inference, PRIS provides an efficient framework for large-scale RNA library screening and aptamer design.