A simple graphical method for determining the inhibition constants of mixed, uncompetitive and non-competitive inhibitors.
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ABSTRACT:
SUBMITTER: Cornish-Bowden A
PROVIDER: S-EPMC1166095 | biostudies-other | 1974 Jan
REPOSITORIES: biostudies-other
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Project description:A new simple graphical method is described for the determination of inhibition type and kinetic parameters of an enzyme reaction without any replot. The method consists of plotting experimental data as v/(vo--v) versus the reciprocal of the inhibitor concentration at different substrate concentrations, where v and vo represent the velocity in the presence and in the absence of the inhibitor respectively with a given concentration of the substrate. Partial inhibition gives straight lines that converge on the abscissa at a point away from the origin, whereas complete inhibition gives lines that go through the origin. The inhibition constants of enzymes and the reaction rate constant of the enzyme-substrate-inhibitor complex can be calculated from the abscissa and ordinate intercepts of the plot. The relationship between the slope of the plot and the substrate concentration shows characteristic features depending on the inhibition type: for partial competitive inhibition, the straight line converging on the abscissa at--Ks, the dissociation constant of the enzyme-substrate complex; for non-competitive inhibition, a constant slope independent of the substrate concentration; for uncompetitive inhibition, a hyperbola decreasing with the increase in the substrate concentration; for mixed-type inhibition, a hyperbola increasing with the increase in the substrate concentration. The properties of the replot are useful in confirmation of the inhibition mechanism.
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Project description:Electrocatalytic reduction of O(2) by functional cytochrome C Oxidase (CcO) models is studied in the presence of several known inhibitors like CO, N(3)(-), CN(-), and NO(2)(-). These models successfully reproduce the inhibitions observed in CcO at similar concentrations reported for these inhibitors. Importantly, the data show very different electrochemical responses depending on the nature of the inhibitor, that is, competitive, non-competitive and mixed. Chemical models have been provided for these observed differences in the electrochemical behavior. Using the benchmark electrochemical behaviors for known inhibitors, the inhibition by NO(2)(-) is investigated. Electrochemical data suggests that NO(2)(-) acts as a competitive inhibitor at high concentrations. Spectroscopic data suggests that NO released during oxidation of the reduced catalyst in presence of excess NO(2)(-) is the source of the competitive inhibition by NO(2)(-). Presence of the distal Cu(B) lowers the inhibitory effect of CN(-) and NO(2)(-). While for CN(-) it weakens its binding affinity to the reduced complex by approximately 4.5 times, for NO(2)(-), it allows regeneration of the active catalyst from a catalytically inactive, air stable ferrous nitrosyl complex via a proposed superoxide mediated pathway.
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Project description:We provide initial rate data from enzymatic reaction experiments and tis processing to estimate the kinetic parameters from the substrate uncompetitive inhibition equation using the median method published by Eisenthal and Cornish-Bowden (Cornish-Bowden and Eisenthal, 1974; Eisenthal and Cornish-Bowden, 1974). The method was denominated the direct linear plot and consists in the calculation of the median from a dataset of kinetic parameters Vmax and Km from the Michaelis-Menten equation. In this opportunity we present the procedure to applicate the direct linear plot to the substrate uncompetitive inhibition equation; a three-parameter equation. The median method is characterized for its robustness and its insensibility to outlier. The calculations are presented in an Excel datasheet and a computational algorithm was developed in the free software Python. The kinetic parameters of the substrate uncompetitive inhibition equation Vmax , Km and Ks were calculated using three experimental points from the dataset formed by 13 experimental points. All the 286 combinations were calculated. The dataset of kinetic parameters resulting from this combinatorial was used to calculate the median which corresponds to the statistic estimator of the real kinetic parameters. A comparative statistical analyses between the median method and the least squares was published in Valencia et al. [3].
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