Average Fusion of Multi-scale Sequence and Multi-track Graph Networks for Protein Function Prediction

• 1、School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122

Abstract：Accurate prediction of protein function is of paramount importance for deciphering biological processes and identifying potential drug targets. While pre-trained protein language models (PLMs) have made significant strides in sequence representation, relying solely on sequence embeddings tends to overlook the three-dimensional topological information of proteins. Conversely, structure-based methods utilizing Graph Neural Networks (GNNs) capture spatial adjacency but may miss the rich contextual information provided by pre-trained models at the semantic level. To leverage these two complementary types of information, this work proposes SGAFGO-a modular, parallel sequence-structure framework. The framework consists of two parallel branches: Multi-scale Sequence Network (MSSN): This branch takes residue embeddings generated by PLMs as input. It extracts multi-scale sequence semantics through parallel 1D convolutions with varying receptive fields and residual connections, aggregating them into a sequence-level prediction.Multi-track Graph Network (MTGN): This branch constructs several parallel tracks (including residual Graph Convolutional, Graph Attention, and learnable pooling tracks) on protein contact maps to learn structure-sensitive graph-level representations from multiple perspectives, generating a structure-level prediction.The two pathways are fused at the prediction level using an arithmetic mean. This fusion strategy introduces no additional learnable parameters and serves as the default approach to reduce the complexity of training and reproduction.Extensive benchmarks and ablation experiments demonstrate that SGAFGO achieves robust performance improvements over both single-path and fusion baselines in standard GO (Gene Ontology) functional annotation tasks; systematic module ablation further validates the complementary mechanisms among components.

Keywords： bioinformatics sequence-structure fusion multi-scale convolutions multi-track graph networks