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  • Brief Communication
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EnzymeML: seamless data flow and modeling of enzymatic data


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

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Fig. 1: Overview of the workflow and tools that were implemented in this work.

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All data availability is listed in Supplementary Table 11.

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All code availability is listed in Supplementary Table 11.


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S.L., H.D., J.P.R. and J.P. were supported by the German Research Foundation under Germany’s Excellence Strategy (EXC 2075, grant 390740016) and by the German Federal Ministry of Education and Research (grant 01DG17027). J.D.S. was supported by the German Research Foundation under Germany’s Excellence Strategy (EXC 2186, grant 390919832). A.W. and U.W. were supported by the Klaus Tschira Foundation and the German Federal Ministry of Education and Research within de.NBI (031A540). T.K. and S.N. were supported by the National Research Foundation of South Africa (grants 105889 and 112099). J.M.R. was supported by the National Research Foundation of South Africa (grant 120859). A.H. and J.W. were partially funded by the Sino-Danish Center for Education and Research and the Technical University of Denmark. C.E.L. and A.S.B. were supported by the US Food and Drug Administration, Center for Drug Evaluation and Research, and Office of Pharmaceutical Quality, through grant U01FD006484. C.E.L. also acknowledges funding by the US National Science Foundation through the Graduate Research Fellowship Program (grant DGE-1650044). F.T.B. was supported by the German Federal Ministry of Education and Research within de.NBI (031L0104A). STRENDA and STRENDA DB are funded by the Beilstein-Institut. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations



S.M., J.D.S., M.F.P., C.E.L., A.V.H. and S.N. contributed data to the scenarios. D.R., P.M., A.S.B., J.M.W. and T.K. supervised the scenarios. F.T.B. and J.M.R. contributed their kinetic modeling platforms. D.I., A.W. and U.W. contributed database platforms. C.K., N.S. and S.S. contributed to the conceptualisation of EnzymeML and to the development of the protocols. S.L., H.D. and J.R. implemented the EnzymeML workflows and analyzed data. J.P. supervised the development and application of EnzymeML workflows and prepared the draft of the manuscript with input from all authors. All authors approved the final version of the manuscript.

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Correspondence to Jürgen Pleiss.

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Nature Methods thanks Shelley Copley, Kenneth Johnson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.

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Lauterbach, S., Dienhart, H., Range, J. et al. EnzymeML: seamless data flow and modeling of enzymatic data. Nat Methods 20, 400–402 (2023).

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