Affiliation:
1. Institute for Biological Interfaces (IBG 1), Karlsruhe Institute of Technology (KIT) , 76344 Eggenstein-Leopoldshafen , Germany
Abstract
Abstract
Enzyme reactions are highly dependent on reaction conditions. To ensure reproducibility of enzyme reaction parameters, experiments need to be carefully designed and kinetic modeling meticulously executed. Furthermore, to enable quality control of enzyme reaction parameters, the experimental conditions, the modeling 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 requirements 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 modeling 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/snapshots/1). 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.
Subject
Clinical Biochemistry,Molecular Biology,Biochemistry
Reference117 articles.
1. Association for Computing Machinery (2021). Artifact review and badging – version 2.0, Available at: https://www.acm.org/publications/policies/artifact-review-badging (Accessed 27 January 2022).
2. Association of Resources for Biophysical Research in Europe and Molecular Biophysics in Europe (no date). Quality control of recombinant protein Best practice recommendations, Available at: https://arbre-mobieu.eu/guidelines-on-protein-quality-control/ (Accessed 27 January 2022).
3. Atkins, W.M. (2005). Non-Michaelis-Menten kinetics in cytochrome P450-catalyzed reactions. Annu. Rev. Pharmacol. Toxicol. 45: 291–310, https://doi.org/10.1146/annurev.pharmtox.45.120403.100004.
4. Ayer, V.M., Miguez, S., and Toby, B.H. (2014). Why scientists should learn to program in Python. Powder Diffr. 29: S48–S64, https://doi.org/10.1017/s0885715614000931.
5. Baici, A. (2015). Kinetics of enzyme-modifier interactions, 1st ed. Vienna: Springer, Vienna.
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