ArticleBMC bioinformatics2011
EnzymeDetector: an integrated enzyme function prediction tool and database.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Comparative Assessment of Protein Large Language Models for Enzyme Commission Number Prediction.BMC bioinformatics · 2025Article
- ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers.Briefings in bioinformatics · 2024Article
- Enzyme Databases in the Era of Omics and Artificial Intelligence.International journal of molecular sciences · 2023Review
- Improving automatic GO annotation with semantic similarity.BMC bioinformatics · 2022Article
- Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation.PLoS computational biology · 2022Article
- Characterizing the breast cancer lipidome and its interaction with the tissue microbiota.Communications biology · 2021Article
- The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases.Metabolites · 2021Article
- GrAPFI: predicting enzymatic function of proteins from domain similarity graphs.BMC bioinformatics · 2020Article
- Utilization of Phenol as Carbon Source by the Thermoacidophilic ArchaeonFrontiers in microbiology · 2020Article
- Alignment-Free Method to Predict Enzyme Classes and Subclasses.International journal of molecular sciences · 2019Article
- BRENDA in 2019: a European ELIXIR core data resource.Nucleic acids research · 2019Article
- Combining multiple functional annotation tools increases coverage of metabolic annotation.BMC genomics · 2018Article
- DEEPre: sequence-based enzyme EC number prediction by deep learning.Bioinformatics (Oxford, England) · 2018Article
- mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning.Frontiers in genetics · 2018Article
- Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone.BMC bioinformatics · 2017Article
- Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats.The ISME journal · 2017Article
- BRENDA in 2017: new perspectives and new tools in BRENDA.Nucleic acids research · 2017Article
- The enzymatic nature of an anonymous protein sequence cannot reliably be inferred from superfamily level structural information alone.Protein science : a publication of the Protein Society · 2015Article
- DomSign: a top-down annotation pipeline to enlarge enzyme space in the protein universe.BMC bioinformatics · 2015Article
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2 authors.
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Abstract
backgroundThe ability to accurately predict enzymatic functions is an essential prerequisite for the interpretation of cellular functions, and the reconstruction and analysis of metabolic models. Several biological databases exist that provide such information. However, in many cases these databases provide partly different and inconsistent genome annotations. DESCRIPTION: We analysed nine prokaryotic genomes and found about 70% inconsistencies in the enzyme predictions of the main annotation resources. Therefore, we implemented the annotation pipeline EnzymeDetector. This tool automatically compares and evaluates the assigned enzyme functions from the main annotation databases and supplements them with its own function prediction. This is based on a sequence similarity analysis, on manually created organism-specific enzyme information from BRENDA (Braunschweig Enzyme Database), and on sequence pattern searches.
conclusionsEnzymeDetector provides a fast and comprehensive overview of the available enzyme function annotations for a genome of interest. The web interface allows the user to work with customisable weighting schemes and cut-offs for the different prediction methods. These customised quality criteria can easily be applied, and the resulting annotation can be downloaded. The summarised view of all used annotation sources provides up-to-date information. Annotation errors that occur in only one of the databases can be recognised (because of their low relevance score). The results are stored in a database and can be accessed at http://enzymedetector.tu-bs.de.
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