Reexamining Michaelis-Menten enzyme kinetics for xanthine oxidase

Molecular Docking Study of IPBCC.08.610 Glucose Oxidase Mutant for Increasing Gluconic Acid Production

Glucose oxidase (GOD) is an oxidoreductase enzyme that catalyzes the oxidation of glucose to gluconolactone and hydrogen peroxide. Then, gluconolactone will be hydrolyzed to gluconic acid. The wide application of gluconic acid in various industries has increased production demand. However, glucose concentrations higher than 40% (w/w) inhibited the conversion of glucose to gluconic acid due to a decrease in the oxygen solubility concentration at pH 6, 30℃, and 1 bar pressure. Therefore, decreasing the value of Km is predicted to reduce saturation and enhance gluconic acid production. This study aimed to analyze the interaction between the IPBCC.08.610 GOD mutant with β-D-Glucose in improving gluconic acid production by decreasing the Km value. Mutations were performed in silico using Chimera and then docked using AutoDock Vina. The mutations resulted in distinct ligand poses in the binding pocket, different -OH conformations of the ligands, and changes in the T554M/D578P mutant’s hydrophobicity index (554 mutated from threonine to methionine, and 578 mutated from aspartate to proline), and decreased ΔG and Km values in the H559D mutant (559 mutated from histidine to aspartate), D578P and T554M/D578P. This decrease might strengthen the ligand-receptor interaction, increasing gluconic acid production. The H559D was the best mutant to increase production based on the ΔG, Km value, and stability due to the addition of hydrogen bonds.

Diverse Data Sets Can Yield Reliable Information through Mechanistic Modeling: Salicylic Acid Clearance

This is a practical example of a powerful research strategy: putting together data from studies covering a diversity of conditions can yield a scientifically sound grasp of the phenomenon when the individual observations failed to provide definitive understanding. The rationale is that defining a realistic, quantitative, explanatory hypothesis for the whole set of studies, brings about a “consilience” of the often competing hypotheses considered for individual data sets. An internally consistent conjecture linking multiple data sets simultaneously provides stronger evidence on the characteristics of a system than does analysis of individual data sets limited to narrow ranges of conditions. Our example examines three very different data sets on the clearance of salicylic acid from humans: a high concentration set from aspirin overdoses; a set with medium concentrations from a research study on the influences of the route of administration and of sex on the clearance kinetics, and a set on low dose aspirin for cardiovascular health. Three models were tested: (1) a first order reaction, (2) a Michaelis-Menten (M-M) approach, and (3) an enzyme kinetic model with forward and backward reactions. The reaction rates found from model 1 were distinctly different for the three data sets, having no commonality. The M-M model 2 fitted each of the three data sets but gave a reliable estimates of the Michaelis constant only for the medium level data (K_m = 24±5.4 mg/L); analyzing the three data sets together with model 2 gave K_m = 18±2.6 mg/L. (Estimating parameters using larger numbers of data points in an optimization increases the degrees of freedom, constraining the range of the estimates). Using the enzyme kinetic model (3) increased the number of free parameters but nevertheless improved the goodness of fit to the combined data sets, giving tighter constraints, and a lower estimated K_m = 14.6±2.9 mg/L, demonstrating that fitting diverse data sets with a single model improves confidence in the results. This modeling effort is also an example of reproducible science available at html://www.physiome.org/jsim/models/webmodel/NSR/SalicylicAcidClearance

High-throughput superoxide anion radical scavenging capacity assay

A high-throughput superoxide anion radical (O2(•-)) scavenging capacity assay based on the xanthine oxidase/xanthine reaction system was developed and validated in the present study. The reaction conditions including detection wavelength, concentrations of reactant components, reaction temperature, reaction time, pH, terminator reagent, and sample dissolving solvents were optimized. The accuracy and reliability of the assay were assessed by evaluation of linearity (r(2) = 0 .9513-0.9957), precision (intraday RSD 1.13-4.05% and interday RSD 2.13-5.62%), accuracy (95.64-97.42% recovery), and stability (RSD 2.62-6.19%), as well as comparison with the conventional colorimetric method. The EC50 values obtained by the current method and the conventional assay were highly correlated (r > 0.99). This high-throughput O2(•-) scavenging assay may be used for screening and estimating potential superoxide anion radical (O2(•-)) scavengers, especially food extracts and natural products with a very small amount of test material.

JSim, an open-source modeling system for data analysis

JSim is a simulation system for developing models, designing experiments, and evaluating hypotheses on physiological and pharmacological systems through the testing of model solutions against data. It is designed for interactive, iterative manipulation of the model code, handling of multiple data sets and parameter sets, and for making comparisons among different models running simultaneously or separately. Interactive use is supported by a large collection of graphical user interfaces for model writing and compilation diagnostics, defining input functions, model runs, selection of algorithms solving ordinary and partial differential equations, run-time multidimensional graphics, parameter optimization (8 methods), sensitivity analysis, and Monte Carlo simulation for defining confidence ranges. JSim uses Mathematical Modeling Language (MML) a declarative syntax specifying algebraic and differential equations. Imperative constructs written in other languages (MATLAB, FORTRAN, C++, etc.) are accessed through procedure calls. MML syntax is simple, basically defining the parameters and variables, then writing the equations in a straightforward, easily read and understood mathematical form. This makes JSim good for teaching modeling as well as for model analysis for research.   For high throughput applications, JSim can be run as a batch job.  JSim can automatically translate models from the repositories for Systems Biology Markup Language (SBML) and CellML models. Stochastic modeling is supported. MML supports assigning physical units to constants and variables and automates checking dimensional balance as the first step in verification testing. Automatic unit scaling follows, e.g. seconds to minutes, if needed. The JSim Project File sets a standard for reproducible modeling analysis: it includes in one file everything for analyzing a set of experiments: the data, the models, the data fitting, and evaluation of parameter confidence ranges. JSim is open source; it and about 400 human readable open source physiological/biophysical models are available at http://www.physiome.org/jsim/.