Partial purification and characterization of acetyl Coenzyme A: Accurate determination of the essential kinetic parameters in the kinetic study of various Michaelis-Menten enzymes within a minimum time scale and handling of huge amount of data is a challenge encountered by the present-day biochemists and molecular biologists when screening an incredible number of mutants for stumbling on an improved strain that produces a kinetically perfect enzyme. In order to check the viability of the algorithm chosen, we initially executed the GA for the two data sets comprising of two enzymes and compared those data with the nonlinear regression and the Lineweaver-Burke results. Although graphical methods help in understanding the kinetic behavior of enzymes, they have certain shortcomings associated with them due to which they sometimes lead to an anomalous estimation of the kinetic parameters. A number of ways of re-arranging the Michaelis-Menten equation have been devised to obtain linear relationships which permit more precise fitting to the experimental points, and estimation of the values of Km and Vmax. The applicability of the stochastic methods was then tested by using the experimental data obtained for an enzyme GAAT.
The kinetic parameters for an enzyme were calculated in presence and absence of enzyme inhibitors David and Cox, whiles those for PI-3 kinase were determined in presence and absence of sodium chloride Michaelis and Menten, If two enzymes, in different pathways, compete for the same substrate, then knowing the values of Km and Vmax for both enzymes permits prediction of the metabolic fate of the substrate and the relative amount that will flow through each pathway under various conditions. If an enzyme is to be used to determine the concentration of substrate in a sample e. If we compare the two evolutionary algorithms , we find that both the algorithms gave almost similar values for Z min the objective function. In PSO, the particles or members of the swarm fly through multidimesional search looking for a potential solution for solving numerical optimization problems. On critical observation and comprehension of the values, it was revealed that GA and PSO analyses, especially the latter, showed similar results: For an enzyme-catalysed reaction, there is usually a hyperbolic relationship between the rate of reaction and the concentration of substrate , as shown below: The determination of enzyme dissociation constants.
Journal of Biological Sciences, 8: It is this visual uncertainty in this plot Fig. With the availability of fast computers and different nonlinear optimization techniques, this task has become fairly simple. We do not claim the superiority of GA and PSO, over the conventional methods, nor do we totally reject the use of conventional techniques.
There are advantages and disadvantages associated with all three main methods of linearising the data.
Mathematically, enzyme kinetics are governed by several reaction constants and parameters like Michaelis-Menten constant K mV maxK cat etc. This page was last edited on 16 Januaryat The wdvantages reciprocal plot distorts the error structure of the data, and it is therefore unreliable for the determination of enzyme kinetic parameters.
The natural phenomenon which forms the basis of GA is the concept of survival of the fittest.
A simple chemical reaction with a single substrate shows a linear relationship between the rate of formation of product and the concentration of substrate, as shown below: Developments, applications and resources. The relationship between rate of reaction and concentration of substrate depends on the affinity of the enzyme for its substrate.
However, since the least square error determination is a non linear function, the usual methods may not be fruitful. In the present study, we considered the option of using two evolutionary algorithms GA and PSO for calculating the MM enzyme parameters K max and V max on a set of five enzymes from literature and the unpublished data set one unique plant enzyme. In order to yield a more accurate estimate of parameters, minimization of least square error can be quite useful. In all other cases the minimum error is less than The values for K m and V max can thus be determined precisely and the error can be estimated quantitatively.
However, one major disadvantage of plotting data in the Lineweaver-Burke mode is that the data points in one region are tightly clustered and tend to propagate small differences, which can be overwhelmed by small random errors, thus giving a non-uniform distribution of error.
The Lineweaver—Burk plot is classically used in older texts, but is prone to error, as the y -axis takes the reciprocal of the rate of reaction — in turn increasing any small errors in measurement. Although graphical methods help in understanding the kinetic behavior of enzymes, they have certain shortcomings associated with them due to which they sometimes lead to an anomalous estimation of the kinetic parameters. The catalytic site of the enzyme is empty, waiting for substrate to bind, for much of the time, and the rate at which product can be formed is limited by the concentration of substrate which is available.
If two enzymes, in different pathways, compete for the same substrate, then knowing the values of Km and Vmax for both enzymes permits prediction of the metabolic fate of the substrate and the relative amount that will flow through each pathway under various conditions.
The crossover and mutation rates are fixed at 0. We compared the performance of aforementioned nonlinear optimization techniques with nonlinear regression Motulsky and Ransnas, method and Lineweaver-Burke and Hanes plots on a set of six enzymes including a unique enzyme birk acetyltransferase GAATpurified from the aromatic grass palmarosa Sharma et al.
Non-competitive inhibition produces plots with the same x -intercept as uninhibited enzyme K m is unaffected but different slopes and y -intercepts. Studies on advantgaes amylases.
In this study, we have used the data from the literature as well as unpublished experimental kinetic data related to a unique plant secondary metabolism enzyme from Palmarosa leaves, named acetyl CoA: If an enzyme is to be used to determine the concentration of substrate in a sample advantagess.
The kinetic parameters K m dissadvantages V max should minimize the quadratic error between observed value and the theoretical value.
Similar Articles in this Journal. The main operators of GA are selection, reproduction and mutation. It increases the precision by linearizing the data. The distribution of output data of enzyme III.
The Lineweaver-Burk double reciprocal plot rearranges the Michaelis-Menten equation as: The fitness of each chromosome is determined by evaluating it against an objective function. GAs were first suggested by Holland This plot overcomes the problem of uneven spacing of points, and undue weight given to points at low concentrations of substrate.
B As the concentration of substrate lineqeaver, the enzyme becomes saturated with substrate. Competitive inhibitors have the same y -intercept as uninhibited enzyme since V max is unaffected by competitive inhibitors the inverse of V max also doesn’t change but there are different slopes and x -intercepts between the two data sets.
I t is difficult to fit the best hyperbola through the experimental points, and difficult to determine Vmax with any precision by estimating the limit of the hyperbola at infinite substrate concentration. How to determine Km and Vmax.
Academic Press, New York. During each iteration, the P vector of the particle with best fitness in the local neighborhood was designated as g and the P vector of the current particle were combined to adjust the velocity along each dimension and a new position of the particle is determined using that velocity.