Back

Force-regulated spontaneous conformational changes of integrins α5β1 and αVβ3

Chen, Y.; Kong, F.; Li, Z.; Ju, L. A.; Zhu, C.

2023-01-09 biophysics
10.1101/2023.01.09.523308 bioRxiv
Show abstract

Force can modulate the properties and functions of macromolecules by inducing conformational changes, such as coiling/uncoiling, zipping/unzipping, and folding/unfolding. Here we compared force-modulated bending/unbending of two purified integrin ectodomains, 5{beta}1 and V{beta}3, using single-molecule approaches. Similar to previously characterized mechano-sensitive macromolecules, the conformation of 5{beta}1 is determined by a threshold head-to-tail tension, suggesting a canonical energy landscape with a deep energy well that traps the integrin in the bent state until sufficient force tilts the energy landscape to accelerate transition to the extended state. By comparison, V{beta}3 exhibits bi-stability even without force and can spontaneously transition between the bent and extended conformations in a wide range of forces without energy supplies. Molecular dynamics simulations revealed consecutive formation and disruption of 7 hydrogen bonds during V{beta}3 bending and unbending, respectively. Accordingly, we constructed an energy landscape with hexa-stable intermediate states to break down the energy barrier separating the bent and extended states into smaller ones, making it possible for the thermal agitation energy to overcome them sequentially and to be accumulated and converted into mechanical work required for V{beta}3 to bend against force. Our study elucidates the different inner workings of 5{beta}1 and V{beta}3 at the sub-molecular level, sheds lights on how their respectively functions are facilitated by their distinctive mechano-sensitivities, helps understand their signal initiation processes, and provides critical concepts and useful design principles for engineering of protein-based biomechanical nanomachines.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.