Influence of the experimental setup on the determination of enzyme kinetic parameters

On the reproducibility of enzyme reactions and kinetic modelling

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.

Three-level hybrid modeling for systematic optimization of biocatalytic synthesis: α-glucosyl glycerol production by enzymatic trans-glycosylation from sucrose

Mechanism-based kinetic models are rigorous tools to analyze enzymatic reactions, but their extension to actual conditions of the biocatalytic synthesis can be difficult. Here, we demonstrate (mechanistic-empirical) hybrid modeling for systematic optimization of the sucrose phosphorylase-catalyzed glycosylation of glycerol from sucrose, to synthesize the cosmetic ingredient α-glucosyl glycerol (GG). The empirical model part was developed to capture nonspecific effects of high sucrose concentrations (up to 1.5 M) on microscopic steps of the enzymatic trans-glycosylation mechanism. Based on verified predictions of the enzyme performance under initial rate conditions (Level 1), the hybrid model was expanded by microscopic terms of the reverse reaction to account for the full-time course of GG synthesis (Level 2). Lastly (Level 3), the application of the hybrid model for comprehensive window-of-operation analysis and constrained optimization of the GG production (~250 g/L) was demonstrated. Using two candidate sucrose phosphorylases (from Leuconostoc mesenteroides and Bifidobacterium adolescentis), we reveal the hybrid model as a powerful tool of "process decision making" to guide rational selection of the best-suited enzyme catalyst. Our study exemplifies a closing of the gap between enzyme kinetic models considered for mechanistic research and applicable in technologically relevant reaction conditions; and it highlights the important benefit thus realizable for biocatalytic process development.