ENZyme KINetics is the study of the chemical reactions that are catalysed by enzymes. In enzyme kinetics the reaction rate is measured and the effects of varying the conditions of the reaction investigated. Studying an enzyme's kinetics in this way can reveal the catalytic mechanism of this enzyme, its role in metabolism, how its activity is controlled, and how a drug or a poison might inhibit the enzyme.
Michaelis–Menten kinetics approximately describes the kinetics of many enzymes. It is named after Leonor Michaelis and Maud Menten. This kinetic model is relevant to situations where very simple kinetics can be assumed, (i.e. there is no intermediate or product inhibition, and there is no allostericity or cooperativity).
The Michaelis–Menten equation relates the initial reaction rate v0 to the substrate concentration [S]. The corresponding graph is a rectangular hyperbolic function; the maximum rate is described as Vmax (asymptote); the concentration of substrate where the v0 is the half of Vmax is the Michaelis-Menten constant (Km).
To determine the maximum rate of an enzyme mediated reaction, a series of experiments is carried out with varying substrate concentration ([S]) and the initial rate of product formation is measured. 'Initial' here is taken to mean that the reaction rate is measured after a relatively short time period,
during which complex builds up but the substrate concentration remains approximately constant and the quasi-steady-state assumption will hold. Accurate values for Km and Vmax can only be determined by non-linear regression of Michaelis-Menten data.
The Michaelis-Menten equation can be linearized using several techniques.
ENZKIN uses 6 regression models (2 non-linear and 4 linear) to obtain the kinetic parameters.
S - data array of substrate concentrations
v - data array of measured initial velocity
- Vmax and Km estimation by:
° Michaelis-Menten non linear regression
° loglog non linear regression
° Lineweaver-Burk linear regression
° Hanes-Woolf linear regression
° Eadie-Hofstee linear regression
° Scatchard linear regression
- for the linear regressions, all regression data are summarized
By itself (without data), enzkin runs a demo