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• W4379967314 abstract "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." @default.
• W4379967314 created "2023-06-10" @default.
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• W4379967314 date "2023-06-09" @default.
• W4379967314 modified "2023-10-01" @default.
• W4379967314 title "Ensemble Reweighting Using Cryo-EM Particle Images" @default.
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• W4379967314 doi "https://doi.org/10.1021/acs.jpcb.3c01087" @default.
• W4379967314 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37293763" @default.
• W4379967314 hasPublicationYear "2023" @default.
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