Kinetic data for the hydrolysis of ONPG catalyzed by Beta-galactosidase (EC. 3.2.1.23)

Description

In the University of Málaga, Enzymology is a second-year subject that all Biochemistry students must take. In the context of this subject, students carry out different experiments in the laboratory, using Beta-galactosidase (EC. 3.2.1.23) as an enzyme model, to illustrate the effect of different variables on the rate of the enzyme-catalyzed reaction (hydrolysis of o-nitrophenyl-Beta-d-galactopyranoside, ONPG). One of these experiments consists in assessing the effect of the substrate (ONPG) concentration on the initial rate. The current dataframe shows the results obtained by eight different student groups, as were presented in their reports.

Usage

ONPG

Format

A dataframe with 10 rows (one per substrate concentration) and 9 columns. The first column give the ONPG concentrations assayed (in mM). The remaining columns provide the determined initial rates. Please, note that rates are given using different units, which can be checked typing in the console: attributes(ONPG).

Kinetic data for Trypanosoma cruzi trans-sialidase.

Description

The variable 'a' is the concentration of p-nitrophenyl alpha-sialoside (as donor) in mM. The variable 'b' is the lactose (as acceptor) concentration in mM. 'rate' is given in mM/min. Biochemistry 2008, 47, 3507–3512 (https://pubmed.ncbi.nlm.nih.gov/18284211)

Usage

TcTS

Format

A dataframe with 16 rows (conditions assayed) and 3 columns (variables).

Kinetic Mechanisms and Parameters for Bi-Bi Reactions

Description

Discriminates between sequential and ping-pong mechanisms and estimates the kinetic parameters.

Usage

bibi(data, unit_a = "mM", unit_b = "mM", unit_v = "ua", vice_versa = FALSE)

Arguments

Details

Either the txt file or the dataframe containing the data must conform to the following format: a table with three columns and as many rows as conditions were assessed. The first and second columns are named 'a' and 'b' and they give the concentrations for substrate A and B, respectively. The third column, named 'rate', provides the assessed rates.

Value

A list with three elements: (i) a character vector giving the kinetic parameters Vmax, KiA, Km_A and Km_B values; (ii) a numeric vector giving the apparent inverse of Vmax for each concentration of substrate B (intercepts of primary representation); and (iii) a numeric vector giving the apparent specificity constant for each concentration of substrate B (slopes from primary representations).

Examples

bibi(data = hk)

Non-linear Least-squares Fitting of the MM equation

Description

Non-linear least-squares fitting of the Michaelis-Menten equation.

Usage

dir.MM(data, unit_S = 'mM', unit_v = 'au', plot = TRUE)

Arguments

Details

This function invokes nls() to carry out the fitting.

Value

A list of two elements. The first one is a vector containing the enzyme kinetic parameters. The second one is a dataframe with the original data plus the fitted value of v.

Examples

dir.MM(ONPG[, c(1,2)])

Eisenthal & Cornish-Bowden

Description

Obtains Km and Vm using the Eisenthal & Cornish-Bowden method.

Usage

ecb(data, unit_S = 'mM', unit_v = 'au', plot = TRUE)

Arguments

Details

For each experimental replicate the observations (S, v) are plotted as lines in the Km-Vm parameter space, instead of points in observation space. Afterwards, the lines tend to intersect at a common point, whose coordinates provide the kinetic parameters. Nevertheless, since the observations are subject to error, there is no unique intersection point for all the lines. In this case, the method computes all the pair-wise intersections. Then, the median value from each series is taken to be the best estimate of Km and Vm. This procedure is repeated as many times as replicates and finally the mean and sd is returned.

Value

Returns a list with the estimated values of Km and Vm.

References

Biochem.J.(1974) 139:715-720 (10.1042/bj1390715)

See Also

lb(), hw(), eh()

Examples

ecb(ONPG[, c(1,2)])

Eadie-Hofstee Transformation

Description

Obtain Km and Vm using the Eadie-Hofstee transformation.

Usage

eh(data, unit_S = 'mM', unit_v = 'au', plot = TRUE)

Arguments

Value

A dataframe with the values of the transformed variables is returned. The fitted Km and Vm are given as attributes of this dataframe.

See Also

lb(), hw(), ecb()

Examples

eh(ONPG[, c(1,2)])

Fitted Progress Curve for Enzyme-Catalyzed Reaction

Description

Fits the progress curve of an enzyme-catalyzed reaction.

Usage

fE.progress(data, unit_S = 'mM', unit_t = 'min')

Arguments

Value

Returns a list with two elements. The first one contains the fitted kinetic parameters, the second one is a dataframe giving the fitted substrate concentration time course.

References

Biochem Mol Biol Educ.39:117-25 (10.1002/bmb.20479).

See Also

sEprogress(), int.MM()

Examples

data <- sE.progress(So = 10, time = 5, Km = 4, Vm = 50, plot = FALSE)
fE.progress(data[, c(1,3)])

Kinetic data for the phosphorylation of glucose catalyzed by hexokinase.

Description

The variable 'a' is the concentration of ATP-Mg2+ in mM. The variable 'b' is the glucose concentration in mM. 'rate' is given in arbitrary units.

Usage

hk

Format

A dataframe with 25 rows (conditions assayed) and 3 columns (variables).

Hanes-Woolf Transformation

Description

Obtains Km and Vm using the Hanes-Woolf transformation.

Usage

hw(data, unit_S = 'mM', unit_v = 'au', plot = TRUE)

Arguments

Value

A dataframe with the values of the transformed variables is returned. The fitted Km and Vm are given as attributes of this dataframe.

See Also

lb(), eh(), ecb()

Examples

hw(ONPG[, c(1,2)])

Linearization of The Integrated Michaelis-Menten Equation

Description

Estimates the kinetic parameters using an linearized form of the integrated Michaelis-Menten equation.

Usage

int.MM(data, unit_S = 'mM', unit_t = 'min')

Arguments

Details

The r-squared value of the model can be checked using attributes().

Value

A list of two elements. The first element is named vector containing the Km and Vm. The second element is a dataframe where the first two columns are the original data and the last two columns are the transformed variables. Also a linear plot of the transformed variables together with the parameters values are provided.

Examples

int.MM(data = sE.progress(So = 10, time = 5, Km = 4, Vm = 50)[, c(1,3)])

Lineweaver-Burk Transformation

Description

Obtains Km and Vm using double reciprocal transformation

Usage

lb(data, unit_S = 'mM', unit_v = 'au', weighting = FALSE, plot = TRUE)

Arguments

Value

A double reciprocal plot and the Km and Vm computed using averaged 1/v (when more than one replicate is provided). In addition, this function returns a list of five elements. The first and second ones are vectors with the Km and Vm, respectively, computed individually for each replicate. The third one provides the R-squared values of the fits. The fourth element of the list gives the fitted Km and Vm. The last element of the list is a dataframe with the values of the transformed variables.

References

J. Am. Chem. Soc.1934, 56, 3,658-666 (doi.org/10.1021/ja01318a036)

See Also

hw(), eh(), ecb()

Examples

lb(ONPG[, c(1,2)], weighting = TRUE)

Progress Curve for Enzyme-Catalyzed Reaction

Description

Simulates the evolution of the substrate concentration along time.

Usage

sE.progress(So, time, Km, Vm, unit_S = 'mM', unit_t = 'min',
                   I = 0, Kic = Inf, Kiu = Inf, replicates = 3,
                   error = 'a', sd = 0.005, plot = TRUE)

Arguments

Details

When sd is different to 0, then an absolute error normally distributed is added to the variable St.

Value

Returns a dataframe where the two first columns are time and St (without error). The two last columns are the mean and sd of the variable St.

See Also

fE.progress()

Examples

sE.progress(So = 10, time = 5, Km = 4, Vm = 50, plot = FALSE)