GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors

G protein-coupled receptors (GPCRs) are central membrane receptors and major therapeutic targets. However, predicting GPCR function remains difficult because experimentally annotated receptors are scarce, functional labels follow a long-tailed distribution, and structural information remains underused. Here, we present GPCR-GO, a relation-aware heterogeneous graph attention framework that integrates structural similarity with protein-protein interactions (PPIs). GPCR-GO uses the Dictionary of Protein Secondary Structure (DSSP) to transform three-dimensional protein structures into residue-level structural descriptors, aggregates these descriptors into protein-level structural vectors, and uses the resulting vectors to define structural-similarity edges. The framework builds a heterogeneous graph linking proteins and Gene Ontology (GO) terms through PPI edges, structural-similarity edges, GO hierarchy edges, and reviewed protein-GO annotations. Relation-aware graph attention aggregates complementary biological signals, whereas graph decomposition, hard negative mining, and semi-supervised learning improve learning under sparse supervision and class imbalance. On the held-out GPCR test split, GPCR-GO outperforms existing methods and achieves F-score (Fmax) values of 0.514, 0.767, and 0.631 on biological process (BP), cellular component (CC), and molecular function (MF), respectively. These results show that structure-derived relations complement curated annotation and support accurate GPCR function prediction under limited supervision.