Our goal is to predict the response of biomolecules to the small, thermal scale, mechanical forces they experience within cells. Active processes in biological systems such as the motion of molecular motors produce piconewton-scale mechanical forces on supramolecular assemblies, and many proteins have evolved to sense and respond to these forces. Yet these forces are very small, and we would not expect them to have a large effect on a protein’s conformation or conformational ensemble. Hence, robustly predicting the response of these <i>mechanosensors</i> at the molecular level is a challenge. Here, I present our efforts to develop enhanced sampling protocols that can allow us to make testable predictions about molecular mechanosensing mechanisms. Our efforts are broadly divided into two areas: predicting the change in conformational ensemble at constant force at equilibrium, for which we have developed an efficient force tempering method known as FISST, and predicting the dissociation rate of protein-protein complexes under force, for which we are employing infrequent metadynamics and weighted ensemble approaches. I will present both our methodological work on test systems as well as application to real mechanosensing proteins, including peptide tension sensors, cytoskeletal protein complexes, and bacterial membrane channels.

Harnessing enhanced sampling molecular dynamics techniques to predict the response of biomolecules to piconewton-scale mechanical forces

ACS Spring 2022

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