EnzymeML: seamless data flow and modeling of enzymatic data.

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 .

SEEK ID: https://publications.h-its.org/publications/1612

PubMed ID: 36759590

Research Groups: Scientific Databases and Visualisation

Publication type: Journal

Journal: Nat Methods

Citation: Nat Methods. 2023 Feb 9. doi: 10.1038/s41592-022-01763-1.

Date Published: 9th Feb 2023

Registered Mode: by PubMed ID

Authors: S. Lauterbach, H. Dienhart, J. Range, S. Malzacher, J. D. Sporing, D. Rother, M. F. Pinto, P. Martins, C. E. Lagerman, A. S. Bommarius, A. V. Host, J. M. Woodley, S. Ngubane, T. Kudanga, F. T. Bergmann, J. M. Rohwer, D. Iglezakis, A. Weidemann, U. Wittig, C. Kettner, N. Swainston, S. Schnell, J. Pleiss

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Created: 14th Feb 2023 at 13:55

Last updated: 5th Mar 2024 at 21:25

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