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  Likelihood-based structural analysis of electron microscopy images

Cossio, P., & Hummer, G. (2018). Likelihood-based structural analysis of electron microscopy images. Current Opinion in Structural Biology, 49, 162-168. doi:10.1016/j.sbi.2018.03.004.

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 Creators:
Cossio, Pilar1, 2, Author           
Hummer, Gerhard1, 3, Author           
Affiliations:
1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
2Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia, ou_persistent22              
3Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany, ou_persistent22              

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 Abstract: Likelihood-based analysis of single-particle electron microscopy images has contributed much to the recent improvements in resolution. By treating particle orientations and classes probabilistically, uncertainties in the reconstruction process are explicitly accounted for, and the risk of bias towards the initial model is diminished. As a result, the quality and reliability of the reconstructions have greatly improved at manageable computational cost. Likelihood-based analysis of electron microscopy images also offers a route to direct coordinate refinement for dynamic systems, as an alternative to 3D density reconstruction. Here, we review recent developments in the algorithms used for reconstructions of high-resolution maps, and in the integrative framework of combining likelihood methods with simulations to address conformational variability in cryo-electron microscopy.

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Language(s): eng - English
 Dates: 20182018-03-242018-04
 Publication Status: Published in print
 Pages: 7
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.sbi.2018.03.004
 Degree: -

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Title: Current Opinion in Structural Biology
  Other : Curr. Opin. Struct. Biol.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Amsterdam : Elsevier
Pages: 7 Volume / Issue: 49 Sequence Number: - Start / End Page: 162 - 168 Identifier: ISSN: 0959-440X
CoNE: https://pure.mpg.de/cone/journals/resource/954925578067