TRGOA: Topological-Aware Residue-Gene Ontology Attention Network for Protein Function Prediction

Protein function prediction is one of the most important biological problems in the field of bioinformatics. The functions of proteins are generally described by a series of Gene Ontology (GO) terms that have hierarchical relationships. Two factors hinder the effective prediction of protein functions using current methods: 1) they cannot well model and learn the topological semantic similarity between residues and GO terms, resulting in a huge semantic gap; 2) they predict the functions of proteins by calculating the semantic similarity between protein-level embeddings and GO terms, which does not effectively learn the protein-function relationship. To address the above issues, we propose the Topological-aware Residue-Gene Ontology Attention Network (TRGOA) for protein function prediction. First, a topological-aware attention module is designed to leverage attention scores within this joint semantic space allowing for modeling the fine-grained semantic similarity between residues and GO terms, thereby narrowing the semantic gap. Second, a multi-head aggregator is proposed, which adeptly captures the functions relevant fine-grained semantic similarity and filters out function-irrelevant components, which effectively reveal protein-function relationships, thereby enhancing generality and robustness. Finally, TRGOA has demonstrated promising outcomes, revealing our model can understand the protein-function relationship in deep insights.