Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-01

AUTHORS

Sjors H W Scheres, Haixiao Gao, Mikel Valle, Gabor T Herman, Paul P B Eggermont, Joachim Frank, Jose-Maria Carazo

ABSTRACT

Although three-dimensional electron microscopy (3D-EM) permits structural characterization of macromolecular assemblies in distinct functional states, the inability to classify projections from structurally heterogeneous samples has severely limited its application. We present a maximum likelihood-based classification method that does not depend on prior knowledge about the structural variability, and demonstrate its effectiveness for two macromolecular assemblies with different types of conformational variability: the Escherichia coli ribosome and Simian virus 40 (SV40) large T-antigen. More... »

PAGES

27-29

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nmeth992

DOI

http://dx.doi.org/10.1038/nmeth992

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015120731

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/17179934


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