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

Abstract

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 framework originally developed for proteins, we introduce BioGraphX-RNA, a universal physico-chemical graph-encoding framework that provides a structure-informed encoding by translating primary nucleotide sequences into multi-scale interaction graphs using explicit biophysical rules. When combined with frozen RiNALMo embeddings via an interpretable gated fusion layer, BioGraphX-RNA achieves competitive performance with DeepLocRNA and uniquely quantifies the relative contribution of sequence versus structure for each RNA. On human datasets, the gated fusion model attains macro-AUROC values of 0.7575 {+/-} 0.0054 (mRNA), 0.9228 {+/-} 0.0137 (miRNA), and 0.5600 {+/-} 0.0191 (lncRNA). For miRNA, the graph-only model alone reaches 0.9396 {+/-} 0.0045, out-performing both the RiNALMo language model and a RNAfold partition-function graph (0.9139 {+/-} 0.0138), validating the structure-informed proxy hypothesis. In a blind cross-species prediction task on mouse data, the model shows limited zero-shot transfer, indicating that biophysical graph features do not improve cross-species generalization. Gating analysis reveals RNA-type-specific modality reliance, with miRNA exhibiting a near-equilibrium balance between sequence and structure. SHAP-based interpretation suggests potential correlates such as patterned GC content for nuclear retention and structural accessibility for exosome targeting. These advances are achieved with only 2.05 million trainable parameters, aligning with Green AI principles. BioGraphX-RNA demonstrates that explicitly integrating biophysical constraints into graph-based encodings enables accurate and interpretable predictions for structured RNAs, advancing structure-aware RNA biology and laying a foundation for precision medicine.