ArticleJournal of structural biology2013
Bayesian analysis of individual electron microscopy images: towards structures of dynamic and heterogeneous biomolecular assemblies.
Article in Journal of structural biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Comparing cryo-EM methods and molecular dynamics simulation to investigate heterogeneity in ligand-bound TRPV1.Nature communications · 2025Article
- The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.bioRxiv : the preprint server for biology · 2025Article
- 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
- InstaMap: instant-NGP for cryo-EM density maps.Acta crystallographica. Section D, Structural biology · 2025Article
- Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference.PLoS computational biology · 2024Article
- High-confidence 3D template matching for cryo-electron tomography.Nature communications · 2024Article
- Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing.International journal of molecular sciences · 2023Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- Reweighting methods for elucidation of conformation ensembles of proteins.Current opinion in structural biology · 2022Review
- Development of hidden Markov modeling method for molecular orientations and structure estimation from high-speed atomic force microscopy time-series images.PLoS computational biology · 2022Article
- Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Visualization of Sparsely-populated Lower-order Oligomeric States of Human Mitochondrial Hsp60 by Cryo-electron Microscopy.Journal of molecular biology · 2021Article
- CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps.Matter · 2021Article
- Recombinant protein linker production as a basis for non-invasive determination of single-cell yeast age in heterogeneous yeast populations.RSC advances · 2021Article
- A Bayesian approach to extracting free-energy profiles from cryo-electron microscopy experiments.Scientific reports · 2021Article
- Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.Annual review of biophysics · 2021Review
- Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution.Frontiers in molecular biosciences · 2021Article
- What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data?Journal of chemical information and modeling · 2020Article
- Conformational ensembles of an RNA hairpin using molecular dynamics and sparse NMR data.Nucleic acids research · 2020Article
- Validation tests for cryo-EM maps using an independent particle set.Journal of structural biology: X · 2020Article
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2 authors.
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Abstract
We develop a method to extract structural information from electron microscopy (EM) images of dynamic and heterogeneous molecular assemblies. To overcome the challenge of disorder in the imaged structures, we analyze each image individually, avoiding information loss through clustering or averaging. The Bayesian inference of EM (BioEM) method uses a likelihood-based probabilistic measure to quantify the consistency between each EM image and given structural models. The likelihood function accounts for uncertainties in the molecular position and orientation, variations in the relative intensities and noise in the experimental images. The BioEM formalism is physically intuitive and mathematically simple. We show that for experimental GroEL images, BioEM correctly identifies structures according to the functional state. The top-ranked structure is the corresponding X-ray crystal structure, followed by an EM structure generated previously from a superset of the EM images used here. To analyze EM images of highly flexible molecules, we propose an ensemble refinement procedure, and validate it with synthetic EM maps of the ESCRT-I-II supercomplex. Both the size of the ensemble and its structural members are identified correctly. BioEM offers an alternative to 3D-reconstruction methods, extracting accurate population distributions for highly flexible structures and their assemblies. We discuss limitations of the method, and possible applications beyond ensemble refinement, including the cross-validation and unbiased post-assessment of model structures, and the structural characterization of systems where traditional approaches fail. Overall, our results suggest that the BioEM framework can be used to analyze EM images of both ordered and disordered molecular systems.
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