Thermodynamic Activity-Based Interpretation of Enzyme Kinetics

Effect of ECM nanostructures in decellularized small intestine on differentiation of intestinal epithelial model cells

Caco-2 cells are derived from human colon cancer and have the ability to differentiate into human intestinal epithelial-like cells. The 2D in vitro intestinal model of Caco-2 cells cultured on a semi-permeable membrane is widely used in drug development and the evaluation of absorption functions. However, these intestinal models lack the structural characteristics of the small intestine in vivo, and the cell behavior is not properly controlled. Previous studies have reported that the microstructure of the villi and crypts on a small intestine-mimicking scaffold promotes Caco-2 differentiation; however, the effect of the nanostructure of the small intestine-mimicking scaffold on Caco-2 differentiation remains unclear. This study aimed to elucidate the effects of nanostructures on the small intestine mimetic scaffold in Caco-2 differentiation. We fabricated a decellularized small intestine in which the basement membrane nanostructure was altered through a subtractive process. Caco-2 cells were cultured on decellularized small intestine for 21 days, and the differentiation of Caco-2 cells was assessed. The microvillus density of Caco-2 cultured on decellularized small intestine that retained the unique nanostructure of small intestinal basement membrane was significantly higher than that of Caco-2 cultured on decellularized small intestine that did not retain the unique nanostructure of small intestinal basement membrane. This indicates that nanostructures specific to the basement membrane of the small intestine enhanced Caco-2 cell maturation.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Boosting the kinetic efficiency of formate dehydrogenase by combining the effects of temperature, high pressure and co-solvent mixtures

The application of co-solvents and high pressure has been shown to be an efficient means to modify the kinetics of enzyme-catalyzed reactions without compromising enzyme stability, which is often limited by temperature modulation. In this work, the high-pressure stopped-flow methodology was applied in conjunction with fast UV/Vis detection to investigate kinetic parameters of formate dehydrogenase reaction (FDH), which is used in biotechnology for cofactor recycling systems. Complementary FTIR spectroscopic and differential scanning fluorimetric studies were performed to reveal pressure and temperature effects on the structure and stability of the FDH. In neat buffer solution, the kinetic efficiency increases by one order of magnitude by increasing the temperature from 25° to 45 °C and the pressure from ambient up to the kbar range. The addition of particular co-solvents further doubled the kinetic efficiency of the reaction, in particular the compatible osmolyte trimethylamine-N-oxide and its mixtures with the macromolecular crowding agent dextran. The thermodynamic model PC-SAFT was successfully applied within a simplified activity-based Michaelis-Menten framework to predict the effects of co-solvents on the kinetic efficiency by accounting for interactions involving substrate, co-solvent, water, and FDH. Especially mixtures of the co-solvents at high concentrations were beneficial for the kinetic efficiency and for the unfolding temperature.

Thermodynamics and Kinetics of Glycolytic Reactions. Part I: Kinetic Modeling Based on Irreversible Thermodynamics and Validation by Calorimetry

In systems biology, material balances, kinetic models, and thermodynamic boundary conditions are increasingly used for metabolic network analysis. It is remarkable that the reversibility of enzyme-catalyzed reactions and the influence of cytosolic conditions are often neglected in kinetic models. In fact, enzyme-catalyzed reactions in numerous metabolic pathways such as in glycolysis are often reversible, i.e., they only proceed until an equilibrium state is reached and not until the substrate is completely consumed. Here, we propose the use of irreversible thermodynamics to describe the kinetic approximation to the equilibrium state in a consistent way with very few adjustable parameters. Using a flux-force approach allowed describing the influence of cytosolic conditions on the kinetics by only one single parameter. The approach was applied to reaction steps 2 and 9 of glycolysis (i.e., the phosphoglucose isomerase reaction from glucose 6-phosphate to fructose 6-phosphate and the enolase-catalyzed reaction from 2-phosphoglycerate to phosphoenolpyruvate and water). The temperature dependence of the kinetic parameter fulfills the Arrhenius relation and the derived activation energies are plausible. All the data obtained in this work were measured efficiently and accurately by means of isothermal titration calorimetry (ITC). The combination of calorimetric monitoring with simple flux-force relations has the potential for adequate consideration of cytosolic conditions in a simple manner.

Combined Linear Interaction Energy and Alchemical Solvation Free-Energy Approach for Protein-Binding Affinity Computation

Calculating free energies of binding (ΔG_bind) between ligands and their target protein is of major interest to drug discovery and safety, yet it is still associated with several challenges and difficulties. Linear interaction energy (LIE) is an efficient in silico method for ΔG_bind computation. LIE models can be trained and used to directly calculate binding affinities from interaction energies involving ligands in the bound and unbound states only, and LIE can be combined with statistical weighting to calculate ΔG_bind for flexible proteins that may bind their ligands in multiple orientations. Here, we investigate if LIE predictions can be effectively improved by explicitly including the entropy of (de)solvation into our free-energy calculations. For that purpose, we combine LIE calculations for the protein-ligand-bound state with explicit free-energy perturbation to rigorously compute the unbound ligand's solvation free energy. We show that for 28 Cytochrome P450 2A6 (CYP2A6) ligands, coupling LIE with alchemical solvation free-energy calculation helps to improve obtained correlation between computed and reference (experimental) binding data.

Combined co-solvent and pressure effect on kinetics of a peptide hydrolysis: an activity-based approach

The application of co-solvents and high pressure has been reported to be an efficient means to tune the kinetics of enzyme-catalyzed reactions. Co-solvents and pressure can lead to increased reaction rates without sacrificing enzyme stability, while temperature and pH operation windows are generally very narrow. Quantitative prediction of co-solvent and pressure effects on enzymatic reactions has not been successfully addressed in the literature. Herein, we are introducing a thermodynamic approach that is based on molecular interactions in the form of activity coefficients of substrate and of enzyme in the multi-component solution. This allowed us to quantitatively predict the combined effect of co-solvent and pressure on the kinetic constants, i.e. the Michaelis constant K_M and the catalytic constant k_cat, of an α-CT-catalyzed peptide hydrolysis reaction. The reaction was studied in the presence of different types of co-solvents and at pressures up to 2 kbar, and quantitative predictions could be obtained for K_M, k_cat, and finally even primary Michaelis-Menten plots using activity coefficients provided by the thermodynamic model PC-SAFT.

Thermodynamic Activity-Based Solvent Design for Bioreactions

To improve the kinetics of enzyme-catalyzed reactions, cosolvents are commonly added to reaction mixtures. The search for a good cosolvent is still empirical and experimentally based. We discuss a thermodynamic activity-based approach that improves biocatalytic processes by predicting cosolvent influences on Michaelis constants, ultimately reducing time and cost.

Simultaneous Prediction of Cosolvent Influence on Reaction Equilibrium and Michaelis Constants of Enzyme-Catalyzed Ketone Reductions

Understanding and quantification of cosolvent influences on enzyme-catalyzed reactions are driven by a twofold interest. On the one hand, cosolvents can simulate the cellular environment for deeper understanding of in cellulo reaction conditions. On the other hand, cosolvents are applied in biotechnology to tune yield and kinetics of reactions. Further, cosolvents are even present inherently, for example, for reactions with cofactor regeneration or for enzymes that need cosolvents in a function of a stabilizer. As the experimental determination of yield and kinetics is costly and time consuming, this work aims at providing a thermodynamic predictive approach that might allow screening cosolvent influences on yield and Michaelis constants. Reactions investigated in this work are the reduction of butanone and 2-pentanone under the influence of 17 wt % of the cosolvent polyethylene glycol 6000, which is also often used as a crowder to simulate cellular environments. The considered reactions were catalyzed by a genetically modified alcohol dehydrogenase (ADH 270). Predictions of cosolvent influences are based on accounting for a cosolvent-induced change of molecular interactions among the reacting agents as well as between the reacting agents and the solvent. Such interactions were characterized by activity coefficients of the reacting agents that were predicted by means of electrolyte perturbed-chain statistical associating fluid theory. This allowed simultaneously predicting the cosolvent effects on yield and Michaelis constants for two-substrate reactions for the first time.

Enzymatic Production of Biodiesel: Strategies to Overcome Methanol Inactivation

Lipase-catalyzed transesterification of triglycerides and alcohols to obtain biodiesel is an environmentally friendly and sustainable route for fuels production since, besides proceeding in mild reaction conditions, it allows for the use of low-cost feedstocks that contain water and free fatty acids, for example non-edible oils and waste oils. This review article reports recent advances in the field and focus in particular on a major issue in the enzymatic process, the inactivation of most lipases caused by methanol, the preferred acyl acceptor used for alcoholysis. The recent results about immobilization of enzymes on nano-materials and the use of whole-cell biocatalysts, as well as the use of cell-surface display technologies and metabolic engineering strategies for microbial production of biodiesel are described. It is discussed also insight into the effects of methanol on lipases obtained by modeling approaches and report on studies aimed at mining novel alcohol stable enzymes or at improving robustness in existing ones by protein engineering.

Co-solvent effects on reaction rate and reaction equilibrium of an enzymatic peptide hydrolysis

This work presents an approach that expresses the Michaelis constant KaM and the equilibrium constant K_th of an enzymatic peptide hydrolysis based on thermodynamic activities instead of concentrations.