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. 2025

    1. T. Giess and J. Pleiss, “Digitalization of biocatalysis: Best practices to research data management,” in Methods in Enzymology. , Academic Press, 2025. doi: https://doi.org/10.1016/bs.mie.2025.01.040.
    2. M. B. M. Spera, S. Darouich, J. Pleiss, and N. Hansen, “Influence of water content on thermophysical properties of aqueous glyceline solutions predicted by molecular dynamics simulations,” Fluid Phase Equilibria, vol. 592, p. 114324, 2025, doi: https://doi.org/10.1016/j.fluid.2024.114324.

  2. 2024

    1. A. Windels, J. Franceus, J. Pleiss, and T. Desmet, “CANDy: Automated analysis of domain architectures in carbohydrate-active enzymes,” PLOS ONE, vol. 19, Art. no. 7, Jul. 2024, doi: 10.1371/journal.pone.0306410.
    2. 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.
    3. 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.
    4. J. Pleiss, “FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science,” ACS Catal., vol. 14, Art. no. 4, Feb. 2024, doi: 10.1021/acscatal.3c06337.
    5. S. Malzacher et al., “The STRENDA Biocatalysis Guidelines for cataloguing metadata,” Nature Catalysis, vol. 7, Art. no. 12, 2024, doi: 10.1038/s41929-024-01261-x.
    6. J. Pleiss, “Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis,” Biochemistry, vol. 63, Art. no. 20, Sep. 2024, doi: 10.1021/acs.biochem.4c00501.
    7. 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.

  3. 2023

    1. S. Lauterbach et al., “EnzymeML: seamless data flow and modeling of enzymatic data,” Nature Methods, vol. 20, 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, 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.

  4. 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, 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, Art. no. 19, Oct. 2022, doi: https://doi.org/10.1111/febs.16318.

  5. 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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