TY - JOUR AU - Lauterbach, Simone AU - Dienhart, Hannah AU - Range, Jan AU - Malzacher, Stephan AU - Spöring, Jan-Dirk AU - Rother, Dörte AU - Pinto, Maria Filipa AU - Martins, Pedro AU - Lagerman, Colton E. AU - Bommarius, Andreas S. AU - Høst, Amalie Vang AU - Woodley, John M. AU - Ngubane, Sandile AU - Kudanga, Tukayi AU - Bergmann, Frank T. AU - Rohwer, Johann M. AU - Iglezakis, Dorothea AU - Weidemann, Andreas AU - Wittig, Ulrike AU - Kettner, Carsten AU - Swainston, Neil AU - Schnell, Santiago AU - Pleiss, Jürgen TI - EnzymeML: seamless data flow and modeling of enzymatic data JO - Nature methods VL - 20 SN - 1548-7091 CY - London [u.a.] PB - Nature Publishing Group M1 - FZJ-2023-01247 SP - 400-402 PY - 2023 AB - 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. LB - PUB:(DE-HGF)16 C6 - 36759590 UR - <Go to ISI:>//WOS:000931967700001 DO - DOI:10.1038/s41592-022-01763-1 UR - https://juser.fz-juelich.de/record/1002265 ER -