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. Analysis of progress curves (time‐course data of substrate depletion or product formation) by different kinetic models sheds light on factors that influence the dynamics of an enzyme‐catalyzed process. This case study compares two previously published experiments to investigate the effect of different kinetic models on the estimated kinetic parameter values, such as for models with and without enzyme inactivation. The results hint at possible pitfalls in model validation to avoid misinterpretation of kinetic parameter values and suggest a consistent data management strategy, supported by the BioCatNet database system.