ArticleScientific reports2024
Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Quasi-continuous cotranslational compaction and folding of a multidomain protein.Nature communications · 2026Article
- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Article
- Structures of protein folding intermediates on the ribosome.Nature structural & molecular biology · 2026Article
- Quasi-continuous cotranslational compaction and folding of a multidomain protein.bioRxiv : the preprint server for biology · 2026Article
- Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- Effects of All-Atom and Coarse-Grained Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of a Tau K18 Monomer.Journal of chemical information and modeling · 2024Article
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6 authors.
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Abstract
Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.
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