ArticleNature communications2024
Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Fast structural search for classification of gut bacterial mucin O-glycan degrading enzymes.PLoS computational biology · 2026Article
- Large language models enhance annotation of enzymes in metagenomes.Science advances · 2026Article
- De novo L-(+)-tartaric acid biosynthesis in multi-modular engineered yeasts.Nature communications · 2026Article
- Advancing generative large language models toward discriminative performance in protein function prediction.Genome biology · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- Evolutionary profile enhancement improves protein function annotation for remote homologs.bioRxiv : the preprint server for biology · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Master of Metals2: a graph neural network based architecture for the prediction of zinc binding sites in protein structures.Briefings in bioinformatics · 2026Article
- CACLENS: A Multitask Deep Learning System for Enzyme Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer.Nature communications · 2026Article
- Accurate proteome-wide prediction of enzymes and catalytic sites using graph deep learning and protein language model.GigaScience · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Classification of virulence factors based on dual-channel neural networks with pre-trained language models.PloS one · 2026Article
- EC-Bench: a benchmark for enzyme commission number prediction.Bioinformatics advances · 2026Article
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Article
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.
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