BioGraphX: Bridging the Sequence-Structure Gap via PhysicochemicalGraph Encoding for Explainable Subcellular Localization Prediction

Computational approaches for protein subcellular localization prediction are important for understanding cellular mechanisms and developing treatments for complex diseases. However, a critical limitation of current methods is their lack of interpretability: while they can predict where a protein localizes, they fail to explain why the protein is assigned to a specific location. Moreover, traditional approaches rely on Anfinsens principle, which assumes that protein behavior is determined by its native three-dimensional structure, requiring costly and time-consuming process. Here, we propose BioGraphX, a novel encoding framework that constructs protein interaction graphs directly from protein sequences using biochemical rules. This approach eliminates the need for three-dimensional structure determination by encoding 158 interpretable features grounded in biophysical principles. Building upon this representation, BioGraphX-Net demonstrates superior performance on the DeepLoc benchmarks by integrating ESM-2 embeddings with the proposed features via a gating mechanism. Gating analysis shows that although ESM-2 embeddings provide strong contributions, BioGraphX features function as high-precision filters. SHAP analysis shows that BioGraphX-Net encodes a sophisticated biophysical logic: sequence profiles act as universal exclusion filters, while organelle-specific combinations of biophysical features enable precise compartment discrimination. Notably, Frustration features help resolve targeting ambiguities in complex compartments, reflecting evolutionary constraints while preventing mislocalization from sequence mimicry. It has the additional advantage of promoting Green AI in bioinformatics, achieving performance comparable to the state-of-the-art while maintaining a minimal parameter count of 13.46 million. In summary, BioGraphX not only provides accurate predictions but also offers new insights into the language of life.