SSAS-GO: structure-sequence adaptive synergy network for protein function prediction

Protein function prediction is essential and fundamental for drug discovery and disease treatment. In recent years, deep learning methods have achieved notable improvements by exploiting either protein sequence or structural features. Specifically, Convolutional Neural Networks often fail to apprehend global protein topologies due to restricted receptive fields, while Graph Convolutional Networks excel at processing graph-structured data, a singular network paradigm fundamentally lacks the capacity to fully integrate diverse, multimodal features. Furthermore, combining modalities via static feature aggregation frequently limits model efficacy and causes modality interference. To resolve these challenges, we propose the Structure-Sequence Adaptive Synergy network (SSAS-GO), which employs a Multi-Scale Motif Block to extract localized sequence semantic anchors, alongside a parallel Dual-Stream Graph Encoder to capture spatial topologies. Subsequently, these representations are fed into a Task-Adaptive Cross-Modal gating mechanism. This core module dynamically recalibrates the weights of sequence and structural features. On the PDBch test set, SSAS-GO leverages native topologies to achieve state-of-the-art Area Under the Precision-Recall Curve (AUPR) scores of 0.463 for Biological Process and 0.559 for Cellular Component. Remarkably, on the AFch test set, the model achieves a substantial 17.7% relative AUPR improvement for Molecular Function tasks.