Standardized Data, Scalable Documentation, Sustainable Storage – EnzymeML As A Basis For FAIR Data Management In Biocatalysis

Abstract

AbstractThe often reported reproducibility crisis in the biomedical sciences also applies to enzymology and biocatalysis, and mainly results from incomplete reporting of reaction conditions. In this Concept article, an infrastructure based on EnzymeML is sketched, which enables reporting, exchange, and storage of enzymatic data according to the FAIR data principles. EnzymeML is a novel data exchange format for enzymology and biocatalysis, which facilitates the application of the STRENDA Guidelines and thus makes data on enzyme‐catalyzed reactions findable, accessible, interoperable, and reusable. EnzymeML enables the comprehensive documentation of metadata, thus fostering reproducibility and replicability in enzymology and biocatalysis. An EnzymeML Application Programming Interface integrates electronic lab notebooks with modelling platforms and databases on enzymatic reactions, and thus enables the seamless flow of enzymatic data from measurement to modelling to publication, without the need for manual intervention such as reformatting or editing. EnzymeML serves as a valuable tool for the design of biocatalytic experiments and contributes to the vision of a unified research data infrastructure for catalysis research.