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S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors
Hailong Wang1,2, Fujun Xiang1,2, Jin Zhang1,2
1Yanzhao Electric Power Laboratory, North China Electric Power University, Baoding, China.
Frontiers in Genetics
|July 23, 2026
Summary
S2DA-GO enhances automated protein function prediction by integrating protein language models and Gene Ontology (GO) semantic priors. This model improves accuracy for sparsely annotated terms and rare labels, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Automated protein function prediction is crucial for annotating vast uncharacterized protein sequences.
- Existing methods struggle with long-tailed multi-label classification, rare labels, and optimization instability.
- Accurate functional annotations are vital for understanding biological systems and disease mechanisms.
Purpose of the Study:
- To develop an advanced computational model, S2DA-GO, for accurate protein function prediction.
- To address challenges in handling sparse annotations and noisy labels within the Gene Ontology (GO) framework.
- To improve the prediction of protein functions, especially for underrepresented GO terms.
Main Methods:
- S2DA-GO utilizes pre-trained protein language model embeddings and GO textual semantic priors.
- A dual-stream architecture (global contextual and local target-aware) captures multi-scale functional patterns.
- A Gradient-Decoupled Cross-Attention Module (GDCAM) mitigates optimization instability from label noise.
Main Results:
- S2DA-GO significantly outperformed the GDTGO baseline across all three GO branches (MF, BP, CC).
- Achieved relative AUPR improvements of 4.0% (MF), 6.0% (BP), and 8.6% (CC).
- Demonstrated robustness in low-homology settings and provided interpretable residue-level signals for binding sites.
Conclusions:
- S2DA-GO effectively alleviates feature interference and enhances prediction for sparsely annotated GO terms.
- The model offers a promising framework for large-scale annotation of uncharacterized proteins.
- S2DA-GO improves the accuracy and reliability of computational protein function prediction.
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