• Corpus ID: 254564573

Ensemble reweighting using Cryo-EM particles

@inproceedings{Tang2022EnsembleRU,
  title={Ensemble reweighting using Cryo-EM particles},
  author={Wai Shing Tang and David Silva-S'anchez and Julian Giraldo-Barreto and Bob Carpenter and Sonya M. Hanson and Alex H. Barnett and Erik H. Thiede and Pilar Cossio},
  year={2022}
}

• Wai Shing Tang, David Silva-S'anchez, +5 authors Pilar Cossio
• Published 10 December 2022
• Chemistry

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