2010Shatsky MultiVariate: Difference between revisions
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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. | 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. | ||
== Keywords == | |||
Heterogeneous reconstruction, heterogeneous data, multi-model reconstruction | |||
== Links == | |||
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2841227/ | |||
Latest revision as of 09:00, 24 September 2013
Citation
Automated Multi-model Reconstruction from Single-Particle Electron Microscopy Data, 2010, Maxim Shatsky, Richard J. Hall, Eva Nogales, Jitendra Malik, Steven E. Brenner, J Struct Biol. 2010 April; 170(1): 98–108
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.
Keywords
Heterogeneous reconstruction, heterogeneous data, multi-model reconstruction