Ensemble Reweighting Using Cryo-EM Particle Images

• Published in: The journal of physical chemistry. B 2023-06, Vol.127 (24), p.5410-5421
• Main Authors: Tang, Wai Shing, Silva-Sánchez, David, Giraldo-Barreto, Julian, Carpenter, Bob, Hanson, Sonya M., Barnett, Alex H., Thiede, Erik H., Cossio, Pilar
• Format: Article
• Language: English
• Subjects: B: Biophysical and Biochemical Systems and Processes; Bayes Theorem; Cryoelectron Microscopy - methods; Molecular Conformation; Molecular Dynamics Simulation; Proteins
• ISSN: 1520-6106; 1520-5207
• DOI: 10.1021/acs.jpcb.3c01087

Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides 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 particle images of a simulated protein that explores multiple folded and unfolded conformations.