Necessary information when publishing

Notes and comments

EC-number
Recommended name of the enzyme
Name of the organism from which the enzyme originates (preferably the binomial name)
Examples: EC 2.5.1.18 – Glutathione transferase,  Mus musculus
EC 7.5.2.25 – ABC-type lipopolysaccharide transporter, Escherichia coli

Recommended names and EC numbers can be found in the recommendations NC-IUBMB, with further details that include names of organisms in Expasy and Brenda, where links to other databases are provided.

Enzyme concentration

Loss of enzyme activity during purification and upon storage is a common phenomenon. We must distinguish between the active center concentration, determined by a titration method and the concentration as protein, which can be determined e.g. photometrically using the absorption coefficient, or by a colorimetric method, e.g. the Bradford assay.

This issue is not trivial because the catalytic constant k_cat, as V/[E]_t (limiting rate divided by the total enzyme concentration),  is often calculated without specifying the method used for determining this concentration or using a value, which does not correspond to the active center concentration determined by titration. As a consequence, the literature contains innumerable k_cat values that, for the same enzyme and assay conditions, span ranges far larger than those expected from experimental error. So, don’t be afraid to present your data using the limiting rate V in place of k_cat since the conclusions will be the same.

In some cases, the ‘k_cat-question’ described on the left becomes enigmatic, suggesting that goblins are at work in laboratories to scramble measurements or calculations. Here an example for the cysteine peptidase cathepsin B, EC 3.4.22.1, from Homo sapiens, substrate Cbz-Phe-Arg-7-amino-4-methylcoumarylamide. In three publications (1-3), the following values for k_cat (s⁻¹) have been reported: 42.2 (1), 364 (2) and 1500 (3). The enzyme active center concentration was measured with the same irreversible inhibitor and all assays conditions were the same.

Temperature
Since the rate of enzyme-catalyzed reactions depends on temperature, this is mandatory information. For enzyme kinetics, a precision of ±1°C is fine.

Despite the importance of temperature, this information is frequently missing in papers dealing with kinetics. AND: “room temperature” is invalid. Further comments are not necessary.

Buffer solutions
All components and counter-ions with their concentrations, as well any other additives should be specified. Some buffer substances have small temperature coefficients (e.g. for phosphate this is −0.0028 per ^oC), while others have large temperature coefficients (e.g. for Tris this is −0.028 per ^oC). Therefore, the temperatures of buffer preparation and that of the assays should always be stated.
Laboratory pH-meters can take into account the temperature at which buffer solutions are prepared, accuracy being ±0.02 pH units and ±0.5°C. It is therefore advisable to show the pH with two decimal places, e.g. pH 7.40. In addition to specifying all species in a solution, its ionic strength should also be reported. Online applications support all necessary calculations.

The policy of preparing concentrated buffers, e.g. 10x, and diluting them 1:10 before assays is not recommended. Depending on the buffer substances, dilution may produce a change of pH, and correcting the pH by adding acid or base will change the ionic strength and composition: not for kinetics!

Reporting statistical results
In publishing enzyme-kinetic results, it is fine to mention the software used. However, the reader will not take advantage of this information unless the procedures are described in due detail. For instance, in case of nonlinear regression, the calculated parameters should be accompanied by the goodness of fit and the results of any runs test performed shown graphically. In particular during the analysis of allosteric modifiers, some parameters con show a high degree of mutual dependency. In this case, specifying any parameter constraints introduced in the regression procedure, showing the covariance matrix and the parameter dependency clarifies any doubt.

Above all, it is important to be aware of the assumptions made when running nonlinear regression (see the list in the column on the right). Such assumptions are important because, if violated, the parameters resulting from the analysis might be erroneous or even meaningless. Changing nonlinear raw data, for which nonlinear regression is the ideal method of analysis, into linear transforms, impairs these assumptions, notably they distort experimental error. Well-known examples are the double-reciprocal plot and the Scatchard plot.

I like very much the following sentence by William Wallace Cleland, pioneer in enzyme kinetics and much more.

… Emphasis should thus be placed on improving precision and repeating experiments a number of times, rather than on more sophisticated techniques for analysis of bad data … And above all, it is important to use your head in making evaluations based on statistical analysis; all mathematics in the world is no substitute for a reasonable amount of common sense. (4, p.138)

The following mathematically erroneous sentence, or variants of it, is found frequently in the literature: Data were fitted by nonlinear regression to equation …
However, the sentence should read: Equation … was fitted to data by nonlinear regression.