EnzymeML-a data exchange format for biocatalysis and enzymology

Area of Science:

• Biochemistry
• Bioinformatics
• Systems Biology

Background:

• Enzymatic data documentation is crucial for reproducibility and data sharing.
• Current methods for exchanging enzymatic data lack standardization, hindering interoperability.
• The Systems Biology Markup Language (SBML) provides a foundation for biological data exchange.

Purpose of the Study:

• To introduce EnzymeML, a standardized XML-based data exchange format for enzymatic data.
• To ensure enzymatic data adheres to FAIR data principles (Findable, Accessible, Interoperable, Reusable).
• To facilitate seamless data transfer between experimental platforms, modeling tools, and databases.

Main Methods:

• EnzymeML is built upon the Systems Biology Markup Language (SBML).
• It incorporates the STRENDA Guidelines for comprehensive documentation of enzymatic data.
• A Python application programming interface (API) is developed for software integration.

Main Results:

• EnzymeML enables comprehensive documentation of reaction conditions, time courses, kinetic models, and constants.
• Demonstrated feasibility of creating EnzymeML documents from spreadsheets and STRENDA DB.
• Successful data transfer and kinetic modeling using COPASI and upload to SABIO-RK.

Conclusions:

• EnzymeML provides a standardized format for enzymatic data exchange, enhancing data accessibility and reusability.
• The format supports a seamless data flow, crucial for advancing biochemical research and modeling.
• EnzymeML promotes adherence to FAIR data principles within the scientific community.