ArticleBioinformatics (Oxford, England)2018
DEEPre: sequence-based enzyme EC number prediction by deep learning.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers.
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101 citing papers in PubMed.
- A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection.International journal of molecular sciences · 2026Review
- Enzyme association for environmental biotransformation reactions through contrastive learning of reaction center-specific fingerprints.Bioinformatics (Oxford, England) · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Trustworthy prediction of enzyme commission numbers using a hierarchical interpretable transformer.Nature communications · 2026Article
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Protein language models uncover carbohydrate-active enzyme function in metagenomics.BMC bioinformatics · 2025Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- RSLpred2: An Integrated Web Server for the Annotation of Rice Proteome Subcellular Localization Using Deep Learning.Rice (New York, N.Y.) · 2025Article
- Semi-supervised data-integrated feature importance enhances performance and interpretability of biological classification tasks.Bioinformatics (Oxford, England) · 2025Article
- Annotating the microbial dark matter with HiFi-NN.iScience · 2025Article
- Review
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
- SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.NAR genomics and bioinformatics · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning.The plant genome · 2025Article
- Comparative Assessment of Protein Large Language Models for Enzyme Commission Number Prediction.BMC bioinformatics · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions.Journal of cheminformatics · 2025Article
41 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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Abstract
Motivation: Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. Results: We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms. Availability and implementation: The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Contact: xin.gao@kaust.edu.sa. Supplementary information: Supplementary data are available at Bioinformatics online.
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