Researchers Dr. Marcelo Melo (Colorado State University, formerly Auburn) and Dr. Rafael Bernardi (Auburn University) have utilized artificial intelligence (AI) and molecular simulations to uncover how certain protein interactions, known as catch-bonds, strengthen under mechanical force. These bonds are crucial in various biological processes, including bacterial adhesion and tissue integrity.
C. R. Melo, M., & Bernardi, R. C. (2025). AI Uncovers the Rapid Activation of Catch-Bonds under Force. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.5c01181
Catch-bonds are unique protein interactions that become stronger when subjected to mechanical stress, akin to a Chinese finger trap tightening under tension. These interactions are essential in processes such as how bacteria attach to our cells and how tissues in our body hold together under stress. However, a fundamental question remained: Do catch-bonds require a specific force threshold to activate, or do they strengthen immediately upon force application?
To investigate this, the team focused on cellulosomes; a bacterial protein complex known for its strong catch-bond properties. Using steered molecular dynamics simulations, they created high-resolution models of these proteins under stress. Subsequently, AI regression models were trained to predict the rupture points of these bonds. Remarkably, the AI models accurately forecasted bond failures using brief segments of simulation data, indicating that the proteins determine their resilience almost immediately upon the application of force.
Dr. Bernardi, Associate Professor of Physics at Auburn University stated,
“This told us that the proteins already ‘decide’ their level of resilience right after the pulling begins. The catch-bond mechanism is activated almost instantly.”
This discovery has significant implications for various fields. Understanding how catch-bonds activate can inform the design of new biomaterials, adhesives, and drug delivery systems that leverage mechanical forces. Moreover, this research highlights the potential of AI in analyzing dynamic biological systems, offering deeper insights into protein behaviors and their applications in medicine and materials science.
The integration of AI with molecular simulations represents a significant advancement in computational biophysics. This study not only enhances our understanding of catch-bonds but also demonstrates the growing role of AI in unraveling complex biological mechanisms, paving the way for innovations in various scientific and medical fields.
Adrian graduated with a Masters Degree (1st Class Honours) in Chemical Engineering from Chester University along with Harris. His master’s research aimed to develop a standardadised clean water oxygenation transfer procedure to test bubble diffusers that are currently used in the wastewater industry commercial market. He has also undergone placments in both US and China primarely focused within the R&D department and is an associate member of the Institute of Chemical Engineers (IChemE).
