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 presented ITCdependent
workflow 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
... mehr
modeling of the enzyme reaction mechanism. Specifically, 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
Gre2pholo 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.