BioGraphX-RNA: A Universal Physicochemical Graph Encoding for Interpretable RNA Subcellular Localization Prediction

RNA subcellular localization is a critical determinant of cellular function. However, current computational approaches often operate as "black boxes," overlooking the complex interplay among sequence, structure, and physicochemical interactions that govern RNA localization. Building upon the BioGraphX originally developed for proteins, we introduce BioGraphX-RNA, a universal physicochemical graph-encoding framework that bridges the sequence-structure divide by translating primary nucleotide sequences into multi-scale interaction graphs grounded in explicit biophysical principles. When integrated with frozen RiNALMo embeddings through an interpretable gated fusion layer, BioGraphX-RNA achieves state-of-the-art performance across all major RNA classes, outperforming DeepLocRNA with macro-AUROC improvements of 0.0172 for mRNA, 0.0545 for miRNA, and 0.0422 for lncRNA on human datasets. In a blind cross-species prediction task on mouse data, the model demonstrates remarkable zero-shot transfer performance, suggesting that biophysical localization cues are evolutionarily conserved. Explainability analyses further reveal RNA-type-specific structural dependencies. Notably, miRNA exhibits a near-equilibrium balance between sequence and structure. SHAP-based interpretations provide mechanistic insights, identifying patterned GC content as a potential nuclear retention signal and an "anti-structure" profile as indicative of exosome-mediated targeting. These advances are achieved with only 2.05 million trainable parameters, aligning with Green AI principles. BioGraphX-RNA therefore demonstrates that explicitly integrating biophysical constraints into graph-based encodings enables accurate, generalizable, and interpretable predictions, advancing structure-aware RNA biology and laying a foundation for precision medicine in RNA-related disorders.