Researchers have used artificial intelligence to help uncover how certain protein interactions act like a finger trap, gripping tighter the harder they are pulled.
These interactions, known as catch-bonds, are essential in how the body holds together under stress and how bacteria attach to cells.
The researchers suggest that a better understanding of these bonds could help inform the design of new medications and biomaterials.
Scientists have been unsure as to whether these catch-bonds activate straight away or if they need to be stretched to a certain threshold before they ‘switch on’.
The new study discovered that these bonds activate almost immediately after a force is applied.
The team ran 200 independent simulations on cellulosomes, a bacterial protein complex with one of the strongest known catch-bond systems in nature. They used a computational microscope that stretches a molecule at the atomic level to create hundreds of high-resolution videos of the protein under stress.
The researchers then trained AI regression models to predict when the protein complex would break. They were surprised to find that the AI could make accurate predictions based on only small amounts of data.
“The catch-bond mechanism is activated almost instantly,” says Dr Rafael Bernardi, associate professor of physics at Auburn University in the US.
“This told us that the proteins already ‘decide’ their level of resilience right after the pulling begins.”
The researchers hope that by uncovering a deeper understanding of how and when these bonds interact, they can provide valuable information to the field of biomedicine.
Previous studies have found that catch-bonds can be spotted throughout the body’s immune system. For example, catch-bonds are a central force that help white blood cells, cells that protect the body from infection and disease, attach to blood vessels.
Bacteria like Staphylococcus aureus, which can cause a wide range of infections from abscesses, pneumonia or sepsis, also uses catch-bond interactions to avoid being washed away.
“These are systems where life has learned to use force as an advantage,” Bernardi explains.
“By learning from them, we can design new biomaterials, adhesives, and even drug strategies that work with mechanical stress instead of against it.”
For Bernardi and the team, the results also demonstrate the power of using AI to assist with complex biological datasets.
“This is exciting because it shows AI can detect early signs of resilience that humans would miss,” says Bernardi.
“That opens the door to using these tools in drug design, biomaterials, and synthetic biology.”
The results from the simulations have been published in the Journal of Chemical Theory and Computation.
“This project shows how physics, biology, and artificial intelligence can come together to answer questions that none of us could solve alone,” says Bernardi.