English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Likelihood-based structural analysis of electron microscopy images

MPS-Authors
/persons/resource/persons145578

Cossio,  Pilar       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia;

/persons/resource/persons15259

Hummer,  Gerhard       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0003-8DB9-E
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.