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  Bayesian analysis of individual electron microscopy images: towards structures of dynamic and heterogeneous biomolecular assemblies

Cossio, P., & Hummer, G. (2013). Bayesian analysis of individual electron microscopy images: towards structures of dynamic and heterogeneous biomolecular assemblies. Journal of Structural Biology, 184(3), 427-437. doi:10.1016/j.jsb.2013.10.006.

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 Creators:
Cossio, Pilar1, 2, Author           
Hummer, Gerhard1, 2, Author           
Affiliations:
1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
2Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, USA, ou_persistent22              

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Free keywords: Bayes Theorem, Bayesian inference, Chaperonin 60, Cryo-EM, Cryoelectron Microscopy, Crystallography, X-Ray, Disorder, Electron microscopy, Ensemble refinement, ESCRT, GroEL, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Likelihood Functions, Microscopy, Electron, Validation
 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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Language(s): eng - English
 Dates: 2013-10-052013-05-022013-10-092013-10-242013-12
 Publication Status: Published in print
 Pages: 11
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jsb.2013.10.006
BibTex Citekey: cossio_bayesian_2013
 Degree: -

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Title: Journal of Structural Biology
  Abbreviation : J. Struct. Biol.
Source Genre: Journal
 Creator(s):
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Publ. Info: San Diego, CA : Elsevier
Pages: - Volume / Issue: 184 (3) Sequence Number: - Start / End Page: 427 - 437 Identifier: ISSN: 1047-8477
CoNE: https://pure.mpg.de/cone/journals/resource/954922650160