Thiamine diphosphate (ThDP)-dependent enzymes catalyze a broad range of reactions with excellent enantioselectivity. Among these reactions, carboligations of aldehydes are of particular interest since the products, chiral hydroxy ketones, are valuable building blocks in the pharmaceutical industry. However, the substrates, for example, benzaldehyde, inactivate the biocatalysts, for example the ThDP-dependent benzaldehyde lyase from Pseudomonas fluorescens (PfBAL). Because only few mechanistic kinetic models for carboligation and simultaneous inactivation are available today, we quantitatively determined the reaction kinetics and inactivation of the self-carboligation of benzaldehyde yielding the product (R)-benzoin catalyzed by PfBAL directly from progress curves using model-based experimental analysis. Discrimination of several inactivation models identified the substrate-dependent inactivation by benzaldehyde to be significant. Sensitivity analysis and optimal experimental design improved parameter precision significantly, to between 4 and 26% relative standard deviation while maintaining the necessary number of 13 experiments moderate. The developed mechanistic kinetic model will enable to perform a model-based process optimization to circumvent the substrate-dependent enzyme inactivation. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018 © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1081-1092, 2018.
The estimation of kinetic parameters provides valuable insights into the function of biocatalysts and is indispensable in optimizing process conditions. Frequently, kinetic analysis relies on the Michaelis-Menten model derived from initial reaction rates at different initial substrate concentrations. However, by analysis of complete progress curves, more complex kinetic models can be identified. This case study compares two previously published experiments on benzaldehyde lyase-catalyzed self-ligation for the substrates benzaldehyde and 3,5-dimethoxybenzaldehyde to investigate 1) the effect of using different kinetic model equations on the kinetic parameter values, and 2) the effect of using models with and without enzyme inactivation on the kinetic parameter values. These analyses first highlight possible pitfalls in the interpretation of kinetic parameter estimates and second suggest a consistent strategy for data management and validation of kinetic models: First, Michaelis-Menten parameters need to be interpreted with care, complete progress curves are necessary to describe the reaction dynamics, and all experimental conditions have to be taken into consideration when interpreting parameter estimates. Second, complete progress curves should be stored together with the respective reaction conditions, to consistently annotate experimental data and avoid misinterpretation of kinetic parameters. Such a data management strategy is provided by the BioCatNet database system.
Progress curve experiments combined with optimal experimental design (OED) are an efficient approach to determine enzyme kinetics. However, it is hardly possible to verify why specific experiments are suggested for nonlinear enzyme kinetic model identification. Therefore, we systematically investigated the surface and contour plots of the sensitivities and of the OED criteria which are based on sensitivities. The model reaction was an enzyme catalyzed self‐ligation of aldehydes to chiral 2‐hydroxyketones. The visualization improved the understanding of OED and allowed for deducing and confirming five suggestions for kinetic identification: (1) Avoid experiments vicinal to the reaction equilibrium, (2) Choose the design space as large as possible, (3) Prefer D(eterminant)‐ and E(igenvalue)‐criteria over the A(verage)‐criterion, (4) Apply enzyme concentrations such that the reaction does not complete too fast, and (5) Few optimal experiments result in significantly improved parameter estimations. The graphical analysis also provides information about selecting appropriate optimization algorithms. © 2017 American Institute of Chemical Engineers AIChE J, 2017
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