ArticleNucleic acids research2023
GotEnzymes: an extensive database of enzyme parameter predictions.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Trade-off between flux efficiency and metabolic control shapes enzyme allocation.Molecular systems biology · 2026Article
- A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells.Cell systems · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Combined Analysis of Bulk and Single-Cell Transcriptomic Data Reveals Dormancy-Associated Genes in Colorectal Cancer.International journal of molecular sciences · 2026Article
- A 15-layer multi-omics analysis of gastric cancer ecotypes provides therapeutic insights.Cell reports. Medicine · 2026Article
- Genome-scale metabolic modeling uncovers cell-type specific signatures associated with APOE variants.iScience · 2026Article
- The proteomic landscape and temporal dynamics of human and mouse gastruloid development.Nature cell biology · 2026Article
- Whole-cell modeling predicts alternative proteome allocation strategies in the archaeon Methanococcus maripaludis.Scientific reports · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.Nucleic acids research · 2026Article
- Systems biology of yeast metabolism.FEMS yeast research · 2026Review
- MetaLigand provides a prior-knowledge-guided framework for predicting non-peptide ligand mediated cell-cell communication.Cell reports methods · 2025Article
- A Comprehensive Review: Current Strategies for Detoxification of Deoxynivalenol in Feedstuffs for Pigs.Animals : an open access journal from MDPI · 2025Review
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Machine Learning Empowering Microbial Cell Factory: A Comprehensive Review.Applied biochemistry and biotechnology · 2025Review
- Minimization of proteome reallocation explains metabolic transition in hierarchical utilization of carbon sources.mSystems · 2025Article
- Novel Data-Driven Mechanistic Modeling of Untargeted Metabolome Data Reveals Feed Component Effects in CHO Cell Bioprocess Using Column Generation-Based EFMs.Biotechnology journal · 2025Article
- Combining Metabolomics and Proteomics to Reveal Key Serum Compounds Related to Canine Intervertebral Disc Herniation.Metabolites · 2025Article
- Computational biology predicts metabolic engineering targets for increased production of 103 valuable chemicals in yeast.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.
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