Disentangling conformational states of macromolecules in 3D-EM through likelihood optimization.
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.
Scheres
Sjors H W
SH
Centro Nacional de Biotecnología, CSIC, Cantoblanco, 28049, Madrid, Spain.
Gao
Haixiao
H
Valle
Mikel
M
Herman
Gabor T
GT
Eggermont
Paul P B
PP
Frank
Joachim
J
Carazo
Jose-Maria
JM
D000952:Antigens, Polyomavirus Transforming / Q000737:chemistry*; D004926:Escherichia coli / Q000737:chemistry; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016013:Likelihood Functions; D008854:Microscopy, Electron / Q000379:methods*; D008958:Models, Molecular; D011487:Protein Conformation; D012270:Ribosomes / Q000737:chemistry*; D012680:Sensitivity and Specificity; D013539:Simian virus 40 / Q000737:chemistry
D000952:Antigens, Polyomavirus Transforming