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Hai-Long Wang

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Open access Jul 2026

S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors

Accurate automated protein function prediction is essential for bridging the widening gap between the exponential accumulation of uncharacterized protein sequences and the limited repository of experimentally verified functional annotations. However, despite recent advances in incorporating Gene Ontology (GO) priors and sequence-label interactions, existing computational methods still face substantial challenges in long-tailed multi-label settings, particularly in handling optimization instability caused by rare-label noise and in learning reliable representations for sparsely annotated GO terms. To address these challenges, we propose S2DA-GO, a sequence-based model using pre-trained protein language model embeddings and GO textual semantic priors for protein function prediction. S2DA-GO integrates a global contextual stream with a local target-aware stream to capture multi-scale functional patterns. To alleviate optimization instability caused by long-tailed label noise, we introduce a Gradient-Decoupled Cross-Attention Module (GDCAM), which reduces the interference of label-specific gradients on the shared backbone. In addition, we incorporate learnable residual semantic priors derived from BioBERT-encoded GO definitions, enhancing the model’s adaptability to rare functional terms. On the benchmark dataset, S2DA-GO outperformed the strong baseline GDTGO across all three GO branches, achieving notable relative AUPR improvements of 4.0% Molecular Function (MF), 6.0% Biological Process (BP), and 8.6% Cellular Component (CC), with AUPR scores reaching 65.1%, 33.4%, and 41.7%, respectively. Notably, S2DA-GO remained robust in low-homology settings and provided interpretable residue-level signals that accurately aligned with experimentally verified binding sites. Overall, S2DA-GO alleviates feature interference and improves prediction for sparsely annotated GO terms, providing a promising framework for large-scale annotation of uncharacterized proteins.

Hai-Long Wang, Fujun Xiang, Jin Zhang et al. · 0 citations
Open access Aug 2026

DHST: A Deep Hybrid Structure–Topology Framework for Accurate Protein Function Prediction

Accurate protein function prediction (PFP) is essential for understanding biological systems. However, structure-based graph neural networks often rely on fixed-distance contact maps, which may inadequately capture continuous, multi-scale spatial topologies, while the long-tail distribution of Gene Ontology (GO) labels may bias prediction toward frequent functions. We propose DHST, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network. DHST further introduces site-specific persistent homology to encode multi-scale topological invariants and a topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings. The fused residue features are aggregated through dual-path pooling, and a weighted binary cross-entropy loss is used to mitigate the adverse effects of label imbalance. On the PDB dataset, DHST achieved area under the precision–recall curve (AUPR) scores of 0.779, 0.481, and 0.557 for molecular function (MF), biological process (BP), and cellular component (CC), respectively; on the AF2 dataset, the corresponding scores were 0.729, 0.390, and 0.459. The model also demonstrated robust generalization to low-homology proteins and maintained strong predictive performance across GO terms with different levels of functional specificity. Ablation results supported the contributions of the main components.

Bin Lu, Fujun Xiang, Hai-Long Wang et al. · 0 citations