Ensemble reweighting using Cryo-EM particles

Wai Shing Tang; David Silva-Sánchez; Julian Giraldo-Barreto; Bob Carpenter; Sonya Hanson; Alex H. Barnett; Erik H. Thiede; Pilar Cossio

arXiv · 2212.05320

Ensemble reweighting using Cryo-EM particles

Wai Shing Tang·David Silva-Sánchez·Julian Giraldo-Barreto·Bob Carpenter·Sonya Hanson·Alex H. Barnett·Erik H. Thiede·Pilar Cossio

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Abstract

Cryo-electron microscopy (cryo-EM) has recently become a premier method for obtaining high-resolution structures of biological macromolecules. However, it is limited to biomolecular samples with low conformational heterogeneity, where all the conformations can be well-sampled at many projection angles. While cryo-EM technically provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot extract the full distribution of possible molecular configurations. To overcome these limitations, we build on a prior Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particles by reweighting a prior ensemble of conformations, e.g., from molecular dynamics simulations or structure prediction tools. Our work is a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM images of a simulated protein that explores multiple folded and unfolded conformations.

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Wai Shing Tang, David Silva-Sánchez, Julian Giraldo-Barreto, Bob Carpenter, Sonya Hanson, Alex H. Barnett, Erik H. Thiede, Pilar Cossio. 2022-12-10. Ensemble reweighting using Cryo-EM particles. https://arxiv.org/abs/2212.05320

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