New! Sign up for our free email newsletter.
Science News
from research organizations

AI helping to quantify enzyme activity

October 19, 2021
Heinrich-Heine University Duesseldorf
Enzymes are biological catalysts that facilitate biochemical transformations. An international team of bioinformatics researchers has developed a new process for predicting Michaelis constants, which determine reaction kinetics.

Without enzymes, an organism would not be able to survive. It is these biocatalysts that facilitate a whole range of chemical reactions, producing the building blocks of the cells. Enzymes are also used widely in biotechnology and in our households, where they are used in detergents, for example.

To describe metabolic processes facilitated by enzymes, scientists refer to what is known as the Michaelis-Menten equation. The equation describes the rate of an enzymatic reaction depending on the concentration of the substrate -- which is transformed into the end products during the reaction. A central factor in this equation is the 'Michaelis constant', which characterises the enzyme's affinity for its substrate.

It takes a great deal of time and effort to measure this constant in a lab. As a result, experimental estimates of these constants exist for only a minority of enzymes. A team of researchers from the HHU Institute of Computational Cell Biology and Chalmers University of Technology in Stockholm has now chosen a different approach to predict the Michaelis constants from the structures of the substrates and enzymes using AI.

They applied their approach, based on deep learning methods, to 47 model organisms ranging from bacteria to plants and humans. Because this approach requires training data, the researchers used known data from almost 10,000 enzyme-substrate combinations. They tested the results using Michaelis constants that had not been used for the learning process.

Prof. Lercher had this to say about the quality of the results: "Using the independent test data, we were able to demonstrate that the process can predict Michaelis constants with an accuracy similar to the differences between experimental values from different laboratories. It is now possible for computers to estimate a new Michaelis constant in just a few seconds without the need for an experiment."

The sudden availability of Michaelis constants for all enzymes of model organisms opens up new paths for metabolic computer modelling, as highlighted by the journal PLOS Biology in an accompanying article.

Story Source:

Materials provided by Heinrich-Heine University Duesseldorf. Original written by Arne Claussen. Note: Content may be edited for style and length.

Journal Reference:

  1. Alexander Kroll, Martin K. M. Engqvist, David Heckmann, Martin J. Lercher. Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLOS Biology, 2021; 19 (10): e3001402 DOI: 10.1371/journal.pbio.3001402

Cite This Page:

Heinrich-Heine University Duesseldorf. "AI helping to quantify enzyme activity." ScienceDaily. ScienceDaily, 19 October 2021. <>.
Heinrich-Heine University Duesseldorf. (2021, October 19). AI helping to quantify enzyme activity. ScienceDaily. Retrieved June 16, 2024 from
Heinrich-Heine University Duesseldorf. "AI helping to quantify enzyme activity." ScienceDaily. (accessed June 16, 2024).

Explore More

from ScienceDaily