Leveraging Residual Graph Convolutional Networks with Cross-Attention Mechanisms for High-Accuracy Protein Function...

Precise determination of protein functions is essential for elucidating cellular processes and pathological mechanisms, thereby facilitating targeted drug design. Although wet-lab experimental methods remain the gold standard to determine protein functions, their long turnaround times, high costs, and labor-intensive procedures make them impractical for large-scale annotation. Here, we introduced RCHGO, a novel deep-learning framework designed to infer Gene Ontology (GO) annotations directly from protein sequences through leveraging residual graph convolutional networks (RGCNs) equipped with cross-attention mechanisms. Comprehensive benchmarking on 1,493 nonredundant proteins demonstrates that RCHGO achieves superior performance compared with 16 state-of-the-art methods. Detailed analyses indicate that the superior performance of RCHGO arises from its two deep learning modules, which separately exploit complementary manually crafted and protein language model-based feature representations and are effectively fused at the decision level. Meanwhile, the integration of RGCNs and cross-attention modules enables the model to learn rich protein- and residue-level representations and align them effectively with GO semantics. The source code of RCHGO is publicly accessible at https://github.com/peixuanli123/RCHGO.