On the reproducibility of enzyme reactions and kinetic modelling

TL;DR

This paper emphasizes the importance of comprehensive, open, and FAIR reporting of enzyme reaction experiments and models to ensure reproducibility, providing practical guidelines and open-source tools for the community.

Contribution

It offers a practical guide and open-source Python-based example for designing enzyme kinetics experiments and modeling, promoting reproducibility and FAIR principles.

Findings

Open, FAIR reporting enhances reproducibility.

Python scripts facilitate transparent enzyme kinetic modeling.

Guidelines improve experimental design and data reporting.

Abstract

Enzyme reactions are highly dependent on reaction conditions. To ensure reproducibility of enzyme reaction parameters, experiments need to be carefully designed and kinetic modelling meticulously executed. Furthermore, to enable the judgement of the quality of enzyme reaction parameters, the experimental conditions, the modelling process as well as the raw data need to be reported comprehensively. By taking these steps, enzyme reaction parameters can be open and FAIR (findable, accessible, interoperable, re-usable) as well as repeatable, replicable and reproducible. This review discusses these issues and provides a practical guide to designing initial rate experiments for the determination of enzyme reaction parameters and gives an open, FAIR and re-editable example of the kinetic modelling of an enzyme reaction. Both the guide and example are scripted with Python in Jupyter Notebooks and are publicly available (https://fairdomhub.org/investigations/483). Finally, the prerequisites of automated data analysis and machine learning algorithms are briefly discussed to provide further motivation for the comprehensive, open and FAIR reporting of enzyme reaction parameters.