Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction

This is the authors' abstract. We don't add key points for this paper.

Protein function prediction has advanced substantially with the integration of AlphaFold2 structural information, yet current models remain constrained by incomplete multimodal feature fusion and limited attention mechanisms for capturing structural-functional relationships. Here, we present an enhanced framework that overcomes these limitations through three innovations: (i) Graph-CBAM, the first adaptation of convolutional block attention to graph neural networks for fine-grained structural feature extraction; (ii) complete multimodal fusion of Node2vec structural embeddings with amino acid one-hot encodings; and (iii) a dual-head self-attention pooling module that stabilizes node importance estimation. Extensive experiments on human protein data sets demonstrate that our model consistently outperforms existing benchmarks across all Gene Ontology branches. We report pronounced improvements, including an increase in Fmax by 2.9% on the Biological Process (BP) branch (0.481 to 0.495) and an enhancement of AUPR by 3.9% on the Cellular Component (CC) branch (0.763 to 0.793). Performance for Molecular Function (MF) remains competitive. Ablation analyses further confirm the independent contributions of structural embeddings, one-hot encodings, and Graph-CBAM. Overall, this work provides a more complete and practical solution for AlphaFold2-based protein function prediction, with particular advantages in predicting functions of proteins lacking protein-protein interaction data.