ArticleNature communications2025
TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- De novo L-(+)-tartaric acid biosynthesis in multi-modular engineered yeasts.Nature communications · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
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Authors and funding
4 authors.
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Abstract
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from enzyme structures and predict Enzyme Commission (EC) classes. Using message-passing frameworks, we include distance and angle information to significantly improve the predictive performance for EC classification (F-score: 0.72) compared to regular 2D graph neural networks. We trained networks without fold bias that can classify enzyme structures for a vast functional space (>800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. We observe that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository on GitHub: https://github.com/IBG4-CBCLab/TopEC and https://doi.org/10.25838/d5p-66 .
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