ArticleBMC bioinformatics2018
ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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70 citing papers in PubMed.
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- How not to be seen: predicting unseen enzyme functions using contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints.Bioinformatics (Oxford, England) · 2026Article
- Complete biosynthesis of psychedelic tryptamines from three kingdoms in plants.Science advances · 2026Article
- How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.bioRxiv : the preprint server for biology · 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
- EC-Bench: a benchmark for enzyme commission number prediction.Bioinformatics advances · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- CAPIM: Catalytic activity and site prediction and analysis tool in multimer proteins.Protein science : a publication of the Protein Society · 2025Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- EZpred: improving deep learning-based enzyme function prediction using unlabeled sequence homologs.bioRxiv : the preprint server for biology · 2025Article
- Merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis.Nature communications · 2025Article
- RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025Article
- Annotating the microbial dark matter with HiFi-NN.iScience · 2025Article
- Comprehensive nucleoside analysis of archaeal RNA modification profiles reveals an mCell reports · 2025Article
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
10 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
backgroundThe automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.
resultsIn ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study.
conclusionsECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred . ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html .
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