Seamlesss data flow by EnzymeML, an SBML-based exchange format for the integration of enzymatic reaction data

PN 2-6

Project description

The goal of research project PN 2-6 is to establish a data management environment for biocatalytic data which

  • enables data acquisition and documentation according to FAIR data principles,
  • provides a searchable data repository for structured data, that can be searched by standardized metadata,
  • enables workflow solutions for a seamless data transfer between experiment, modelling platforms, and databases with minimal human intervention.

The core of the project is the development of EnzymeML as a standardized exchange format for biocatalytic data.

Project information

Project title

Seamlesss data flow by EnzymeML, an SBML-based exchange format for the integration of enzymatic reaction data
Project leader Jürgen Pleiss
Project partners Nicole Radde (PN2-9)

Tobias Siebert, Oliver Röhrle (PN2-8)
Grazia Lamanna, Rico Poser (PN1-3)
Dorothea Iglezakis (FoKUS)
Project duration February 2021 - July 2021
Project number PN 2-6
  • Follow-up project 2-6 (II)
    Software-driven RDM (sdRDM), a generic and extensible bottom-up research data management concept and its application in biocatalysis and beyond

Publications PN 2-6 and PN 2-6 (II)

  1. 2024

    1. H. F. Carvalho, L. Mestrom, U. Hanefeld, and J. Pleiss, “Beyond the Chemical Step: The Role of Substrate Access in Acyltransferase from Mycobacterium smegmatis,” ACS Catal., vol. 14, pp. 10077--10088, Jun. 2024, doi: 10.1021/acscatal.4c00812.
    2. F. Neubauer, P. Bredl, M. Xu, K. Patel, J. Pleiss, and B. Uekermann, “MetaConfigurator: A User-Friendly Tool for Editing Structured Data Files,” Datenbank-Spektrum, vol. 24, pp. 161–169, 2024, doi: 10.1007/s13222-024-00472-7.
    3. J. Pleiss, “FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science,” ACS Catal., vol. 14, no. 4, Art. no. 4, Feb. 2024, doi: 10.1021/acscatal.3c06337.
    4. B. Flemisch et al., “Research Data Management in Simulation Science: Infrastructure, Tools, and Applications,” Datenbank-Spektrum, 2024, doi: https://doi.org/10.1007/s13222-024-00475-4.

  2. 2023

    1. S. Lauterbach et al., “EnzymeML: seamless data flow and modeling of enzymatic data,” Nature Methods, vol. 20, no. 3, Art. no. 3, 2023, doi: 10.1038/s41592-022-01763-1.
    2. T. Giess, S. Itzigehl, J. Range, R. Schömig, J. R. Bruckner, and J. Pleiss, “FAIR and scalable management of small-angle X-ray scattering data,” Journal of Applied Crystallography, vol. 56, no. 2, Art. no. 2, Apr. 2023, doi: 10.1107/S1600576723001577.
    3. S. Höpfl, J. Pleiss, and N. E. Radde, “Bayesian estimation reveals that reproducible models in Systems Biology get more citations,” Scientific reports, vol. 13, p. 2695, 2023, doi: 10.1038/s41598-023-29340-2.

  3. 2022

    1. A. Mack et al., “Preferential Self-interaction of DNA Methyltransferase DNMT3A Subunits Containing the R882H Cancer Mutation Leads to Dominant Changes of Flanking Sequence Preferences,” Journal of Molecular Biology, vol. 434, no. 7, Art. no. 7, 2022, doi: 10.1016/j.jmb.2022.167482.
    2. J. Range et al., “EnzymeML—a data exchange format for biocatalysis and enzymology,” The FEBS Journal, vol. 289, no. 19, Art. no. 19, Oct. 2022, doi: https://doi.org/10.1111/febs.16318.

  4. 2021

    1. J. Pleiss, “Standardized data, scalable documentation, sustainable storage –  EnzymeML as a basis for FAIR data management in biocatalysis,” ChemCatChem, vol. 13, pp. 3909–3913, 2021, doi: https://doi.org/10.1002/cctc.202100822.
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