Energy landscapes from single-particle imaging of biological processes in and out of equilibrium

Ideally, a measurement of the free energy landscape of a biomolecule would combine the sampling of a macroscopic ensemble with the microscopic information of molecular simulation. We have developed a graph-theoretic technique which allows us to map the low-dimensional conformational manifold embedded in noisy single-particle cryo-electron microscope images[1,2]. Recently we successfully applied the method to X-ray Free Electron Laser diffraction images to map the process of genome release of the PR772 virus[3]. In all cases, we find continuous structural pathways on the extracted landscape which correspond directly to known biological function. Processes such as viral infection are highly non-equilibrium; in principle, the underlying free energy landscape can be derived from such non-equilibrium measurements, and we demonstrate this possibility in simple models. Single-particle imaging promises unprecedented access to the energy landscapes of biological systems.

[1] Trajectories of the ribosome as a Brownian nanomachine. PNAS (2014).

[2] Conformational Dynamics and Energy Landscapes of Ligand Binding in RyR1. bioRxiv (2017).

[3] Conformational landscape of a virus by single-particle X-ray scattering. Nat. Methods (2017).