Automated data exchange format creates transparency in enzymatic experiments

Credit: natural methods (2023). DOI: 10.1038/s41592-022-01763-1

In the catalytic sciences, as in all scientific fields, we are faced with a rapidly increasing amount and complexity of research data, which poses challenges for analysis and reuse. A team led by Prof. Jürgen Pleiss from the Institute for Biochemistry and Technical Biochemistry at the University of Stuttgart presented EnzymeML as a data exchange format in a recently published journal article natural methods. EnzymeML serves as a format to comprehensively report the results of an enzymatic experiment and stores the data in a structured way to make it traceable and reusable.

While more and more data is being generated by more and more researchers worldwide and research expenditure is increasing, this data can hardly be managed with the usual scientific practice of communicating scientific results. Even managing your own data manually is time-consuming and error-prone, but accessing and reanalyzing data from other research groups is nearly impossible. Missing standards, incomplete metadata and missing original data make it almost impossible to reproduce published results. More and more researchers feel like they are drowning in a data tsunami.

This also applies to investigations into the catalytic activity, selectivity and stability of enzymes and enzymatic networks, a research area that is equally important for industrial biotechnology and biomedicine. To make matters worse, the data on enzymatic experiments is particularly complex, since an enzymatic reaction depends on many factors, such as the protein sequence of the enzyme, the recombinant host organism, the reaction conditions and non-enzymatic side reactions. In addition, other effects such as inactivation or inhibition of the enzyme or evaporation of the medium influence the results.

The new, standardized data exchange format EnzymeML, presented in the journal by 23 authors from 14 different research institutions natural methods addresses this dilemma. EnzymeML can fully record the results of an enzymatic experiment, from the reaction conditions to the measured data, as well as the kinetic model used to analyze the experimental data and the estimated kinetic parameters. The format thus provides a seamless communication channel between experimental platforms, electronic laboratory notebooks, enzyme kinetics modeling tools, publishing platforms and enzymatic reaction databases.

“We demonstrate the feasibility and usefulness of the EnzymeML toolbox using six scenarios in which data and metadata from different enzymatic reactions are collected, analyzed and uploaded to public databases for future use,” explains first author Simone Lauterbach.

EnzymeML documents are structured and standardized, therefore the experimental results encoded in an EnzymeML document are interoperable and reusable by other groups. Because an EnzymeML document is machine-readable, it can be used in an automated workflow for storing, visualizing, and analyzing data, as well as re-analyzing previously published data, with no limitations on the size of each data set or the number of experiments.

“The digitization of biocatalysis increases the efficiency of data management, visualization and analysis,” says Prof. Jürgen Pleiss, corresponding author and project coordinator. In addition, digitization improves the reproducibility of experiments and data analysis, thus promoting confidence in scientific results. “The EnzymeML toolbox makes the most of the fast-growing enzymatic data base and is a useful tool for researchers to ride the research data wave.”

More information:
Simone Lauterbach et al, EnzymeML: seamless data flow and modeling of enzymatic data, natural methods (2023). DOI: 10.1038/s41592-022-01763-1

Provided by the University of Stuttgart

Citation: Automated data exchange format creates transparency in enzymatic experiments (2023 February 13), retrieved February 13, 2023 from

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