R3J-AGNN: GNN-Based Prediction of Inter-Branch Angles in RNA Three-Way Junctions from Secondary Structure.

Despite advances in computational RNA 3D structure prediction, accurately modeling multi-branch RNA motifs remains challenging, even with known secondary structures, because their global topology is highly sensitive to the relative orientations of the helical stems at the junction. Here, we propose R3J-AGNN, a dual-resolution hierarchical graph neural network that predicts inter-branch angles of RNA three-way junctions (3WJs) directly from secondary structure information. The model integrates fine-grained nucleotide-level interactions with a coarse-grained representation of the global topology of 3WJ-containing RNA structures, enabling accurate inference of inter-branch geometry and subsequent reconstruction of their 3D scaffolds. Evaluations on an independent test set demonstrate that R3J-AGNN achieves robust and consistent predictive performance. Thus, by inferring inter-branch angles of RNA three-way junctions from secondary structure information, R3J-AGNN provides actionable geometric constraints for RNA tertiary structure modeling and refinement.