ArticleNature methods2023
EnzymeML: seamless data flow and modeling of enzymatic data.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026Review
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Best Practices for Machine Learning-Assisted Protein Engineering.Journal of chemical information and modeling · 2025Review
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Machine learning applied to biocatalysis research.Nature communications · 2025Article
- Finding the dark matter: Large language model-based enzyme kinetic data extractor and its validation.Protein science : a publication of the Protein Society · 2025Article
- Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.Nature communications · 2025Article
- Activity assays for flavoprotein oxidases: an overview.Applied microbiology and biotechnology · 2025Review
- Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering.ACS central science · 2024Review
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Enzyme Databases in the Era of Omics and Artificial Intelligence.International journal of molecular sciences · 2023Review
- Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design.ACS catalysis · 2023Review
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
- Modelling the Decamerisation Cycle of PRDX1 and the Inhibition-like Effect on Its Peroxidase Activity.Antioxidants (Basel, Switzerland) · 2023Article
- Ultrahigh-Throughput Enzyme Engineering and Discovery inChemical reviews · 2023Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
Indexed as
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36759590What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.