Journal Article FZJ-2023-01247

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EnzymeML: seamless data flow and modeling of enzymatic data

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2023
Nature Publishing Group London [u.a.]

Nature methods 20, 400-402 () [10.1038/s41592-022-01763-1]

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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.

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Contributing Institute(s):
  1. Biotechnologie (IBG-1)
Research Program(s):
  1. 2171 - Biological and environmental resources for sustainable use (POF4-217) (POF4-217)

Appears in the scientific report 2023
Database coverage:
Medline ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DEAL Nature ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 40 ; JCR ; NationallizenzNationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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 Record created 2023-02-21, last modified 2023-09-29


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