Toward Reproducible Enzyme Modeling with Isothermal Titration Calorimetry

To apply enzymes in technical processes, a detailed understanding of the molecular mechanisms is required.
Kinetic and thermodynamic parameters of enzyme catalysis are crucial to plan, model, and implement biocatalytic
processes more efficiently. While the kinetic parameters, Km and kcat, are often accessible by optical methods, the
determination of thermodynamic parameters requires more sophisticated methods. Isothermal titration calorimetry
(ITC) allows the label-free and highly sensitive analysis of kinetic and thermodynamic parameters of individual steps
in the catalytic cycle of an enzyme reaction. However, since ITC is susceptible to interferences due to denaturation
or agglomeration of the enzymes, the homogeneity of the enzyme sample must always be considered, and this can
be accomplished by means of dynamic light scattering (DLS) analysis. The here presented ITC-dependent
workflow[1] was used to determine both the kinetic and the thermodynamic data for a cofactor-dependent enzyme.
Using a standardized approach with the implementation of sample quality control by DLS, we obtained high-quality
data suitable for the advanced modeling of the enzyme reaction mechanism. ... mehrSpecifically, we investigated
stereoselective reactions catalyzed by the NADPH-dependent ketoreductase Gre2p under different reaction
conditions. The results revealed that this enzyme operates with an ordered sequential mechanism in which the
cofactor NADPH binds first, resulting in Gre2pholo, and that only Gre2p(holo) can then bind the substrate NDK. In
addition, the enzyme was found to be affected by substrate or product inhibition depending on the reaction buffer.
Data reproducibility, a mandatory prerequisite to achieve robust modeling, is ensured by specifying standard
operating procedures, using programmed workflows for data analysis and storing all data in a F.A.I.R. (findable,
accessible, interoperable, and reusable) repository. Our approach highlights the utility for combined binding and
kinetic studies for such complex multisubstrate reactions. Because it is amenable to automation and scale-up for
high-throughput, the combination of such diverse approaches will provide the high-quality data needed for the
engineering of enzymes and biocatalytic processes through machine learning to accelerate future development of
industrial biocatalysis.