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Enzymes should kiss and run

Enzymes fit their substrates snugly to be specific - yet should not get trapped after they have done their deed. Therefore, enzymes bind their partners with intermediate strength to “kiss-and-run”. Researchers at Aarhus University have developed equations that predict the optimal binding strength for enzyme docking interactions.

[Translate to English:] Magnus & Nico
Nicolas Sebastian Gonzalez Foutel (left) and Magnus Kjærgaard have developed equations that predict the optimal binding strength for enzyme docking interactions (photo: Lisbeth Heilesen)

Enzymes are used extensively to produce complicated natural compounds and reducing the environmental footprint of chemical manufacturing, and hence it is important to know how they function. 

Enzymes are the backbone of cell signalling. For signals to reach the right recipient, enzymes need to distinguish the right partner from thousands of other molecules. Substrate recognition by enzymes is often compared to a lock where only one specific key fits.

Testing thousands of wrong locks take a lot of time. Therefore, many signalling enzymes have additional docking interactions that allow them to home-in on the right substrate.

“If the substrate is a key in search of a lock, then the docking interaction can be thought of as a GPS signal that directs it to the right street”, Associate Professor Magnus Kjærgaard explains. “By targeting the key to the right neighbourhood, the number of locks to be searched is dramatically reduced”.

Docking interactions are faced with a dilemma. If they bind weakly, substrates are not targeted to the enzyme. If they bind tightly, the substrate stays stuck to the enzyme and prevents it from moving on to the next substrate.

“Docking interactions should neither be too tight or too weak, but just right. Like the temperature of the porridge in the fairy tale about Goldilocks”, Magnus adds. “We want to predict exactly where this Goldilocks zone is”.

Bridging experiments and theory

The researchers studied a protein kinase, a common type of enzyme that relays signals in cells. To study the role of docking strength, it was varied more than a thousand-fold by changing single amino acids in the interface between enzyme and substrate.

“When we decrease the binding strength, the catalytic efficiency first goes up. But if we keep decreasing it, it reaches a summit and eventually decrease again”, co-first author Nicolas Gonzalez-Foutel explains. “This observation confirms the prediction of a Goldilocks zone, where the binding strength is ‘just right’.”

To be able to generalize to other systems it was necessary to build a theoretical model of the reaction.

“By simulating the system, we can change the connection between enzyme and substrate, or the type of substrate. This allows us the study many more cases than we could ever manage experimentally”, co-first author Mateusz Dyla explains. “We derived an equation that predicts the docking strength that maximize the efficiency of the enzyme. For the first time, this allows us to state precisely where the Goldilocks zone is for a specific enzyme.”

More than just an academic exercise

The researchers believe the equations may be general and have applications in biotechnology. Enzymes are used extensively to produce complicated natural compounds and reducing the environmental footprint of chemical manufacturing.

“The simulations do not know that they are simulating a kinase. As far as the theory is concerned, it could be any type of enzyme. Therefore, we think the model will apply to many other enzymes that use similar docking interactions,” Magnus suggests. “We hope to understand the ‘design principles’ of enzyme docking interactions. This will allow us to predict which docking interactions would enhance a given enzyme most.”


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The researchers declare that there are no conflicts of interest.

Link to scientific paper

Title: The optimal docking strength for reversibly tethered kinases

Authors: Mateusz Dyla, Nicolás S. González Foutel, Daniel E. Otzen and Magnus Kjaergaard

Journal: PNAS
DOI: 
https://doi.org/10.1073/pnas.220309811

Contact information

Associate Professor Magnus Kjærgaard
Department of Molecular Biology and Genetics
Aarhus University, Denmark
magnus@mbg.au.dk