ArticleProceedings of the National Academy of Sciences of the United States of America2019
Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.
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Who cites it
115 citing papers in PubMed.
- EINN: An enzyme-informed neural network guided by an enzyme-constrained genome-scale metabolic model.Synthetic and systems biotechnology · 2027Article
- A Novel Unsupervised Learning Model with Bayesian Hyperparameter Optimization for Predicting Substrates of Promiscuous Enzymes.ACS omega · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- Large language models enhance annotation of enzymes in metagenomes.Science advances · 2026Article
- WASP: a pipeline for functional annotation prediction based on AlphaFold structural models.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
- How not to be seen: predicting unseen enzyme functions using contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.Acta pharmaceutica Sinica. B · 2026Article
- High yield strategies for triterpenoid biosynthesis in cell factories.Journal of microbiology (Seoul, Korea) · 2026Review
- Interpretable convolutional neural networks for sequence-based classification and discovery of plastic-degrading enzymes.Applied and environmental microbiology · 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
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- From hidden allies to precision symbionts: unleashing endophytes for sustainable agroecosystems.World journal of microbiology & biotechnology · 2026Review
- Dual-encoder contrastive learning accelerates enzyme discovery.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.bioRxiv : the preprint server for biology · 2026Article
- Approaches for accelerating microbial gene function discovery using artificial intelligence.Nature microbiology · 2026Review
- 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
55 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers that cannot be classified by the CNNs. Comparative analyses against 5 representative EC number prediction tools show that DeepEC allows the most precise prediction of EC numbers, and is the fastest and the lightest in terms of the disk space required. Furthermore, DeepEC is the most sensitive in detecting the effects of mutated domains/binding site residues of protein sequences. DeepEC can be used as an independent tool, and also as a third-party software component in combination with other computational platforms that examine metabolic reactions.
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