Our current way to do biocatalytic research and development is still limited by low reproducibility of experimental results, limited scalability of experimentation, and limited access to data. Even if accessible, data and metadata cannot be easily exchanged between research groups because of missing interoperability, which needs additional efforts for reformatting and results in loss of information. Limited reproducibility and interoperability severely hampers scientific development and causes additional costs and delays in academic research and industrial development.
In a cooperation with project partners in Europe, USA, and South Africa, we currently establish EnzymeML as a novel standardized data exchange format for biocatalysis, which facilitates the application of the STRENDA Guidelines and thus makes data on enzyme-catalyzed reactions findable, accessible, interoperable, and reusable (FAIR). An Application Programming Interface enables the integration of applications such as electronic lab notebooks, modelling platforms, and databases. Documentation and software of the EnzymeML project are freely available for non-commercial and commercial users.
We also contribute the EnzymeML platform to the members of the COST Action COZYME.
This research project is funded in the framework of the Stuttgart Cluster of Excellence SimTech.
Publications
- Behr, A.S., Surkamp, J., Abbaspour, E., Häußler, M., Lütz, S., Pleiss, J., Kockmann, N., Rosenthal, K.: Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies. Processes. 12, (2024). https://doi.org/10.3390/pr12030597.
- Pleiss, J.: FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science. ACS Catal. 14, 2709--2718 (2024). https://doi.org/10.1021/acscatal.3c06337.
- Lauterbach, S., Dienhart, H., Range, J., Malzacher, S., Spöring, J.-D., Rother, D., Pinto, M.F., Martins, P., Lagerman, C.E., Bommarius, A.S., Høst, A.V., Woodley, J.M., Ngubane, S., Kudanga, T., Bergmann, F.T., Rohwer, J.M., Iglezakis, D., Weidemann, A., Wittig, U., Kettner, C., Swainston, N., Schnell, S., Pleiss, J.: EnzymeML: seamless data flow and modeling of enzymatic data. Nature Methods. 20, 400--402 (2023). https://doi.org/10.1038/s41592-022-01763-1.
- Höpfl, S., Pleiss, J., Radde, N.E.: Bayesian estimation reveals that reproducible models in Systems Biology get more citations. Scientific reports. 13, 2695 (2023). https://doi.org/10.1038/s41598-023-29340-2.
- Range, J., Halupczok, C., Lohmann, J., Swainston, N., Kettner, C., Bergmann, F.T., Weidemann, A., Wittig, U., Schnell, S., Pleiss, J.: EnzymeML—a data exchange format for biocatalysis and enzymology. The FEBS Journal. 289, 5864--5874 (2022). https://doi.org/10.1111/febs.16318.
- Pleiss, J.: Standardized data, scalable documentation, sustainable storage – EnzymeML as a basis for FAIR data management in biocatalysis. ChemCatChem. 13, 3909–3913 (2021). https://doi.org/10.1002/cctc.202100822.
- Malzacher, S., Range, J., Halupczok, C., Pleiss, J., Rother, D.: BioCatHub, a graphical user interface for standardized data acquisition in biocatalysis. Chem Ing Tech. 92, 1251–1251 (2020). https://doi.org/10.1002/cite.202055297.
Project Members
Jan Range
Bioinformatics
Max Häußler
Bioinformatics
Torsten Giess
Bioinformatics