Derivation and identification of a mechanistic model for a branched enzyme-catalyzed carboligation.

The kinetic description of enzyme-catalyzed reactions is a core task in biotechnology and biochemical engineering. In particular, mechanistic kinetic models help from the discovery of the biocatalyst throughout its application. Chemo- or enantioselective enzyme reactions often undergo two alternative pathways for the release of two different products from a central intermediate. For these types of reaction, no explicit reaction equations have been derived so far. To this end, we extend the commonly used Cleland's notation for branched reaction pathways and explicitly derive the rate expressions for two-coupled ordered bi-uni reactions. This mechanism also leads to a ping-pong bi-bi mechanism for a transfer reaction between the two products via the same central intermediate of the reaction system. Using the cross-ligation of benzaldehyde and propanal catalyzed by the thiamine diphosphate-dependent enzyme benzaldehyde lyase from Pseudomonas fluorescens yielding (R)-2-hydroxy-1-phenylbutan-1-one as a case study, we performed model-based experimental analysis to show that such a reaction mechanism can be modeled mechanistically and leads to reasonable kinetic parameters.

Progress Curve Analysis Within BioCatNet: Comparing Kinetic Models for Enzyme-Catalyzed Self-Ligation.

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.

Simultaneous identification of reaction and inactivation kinetics of an enzyme-catalyzed carboligation.

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.