Maximum likelihood refinement of electron microscopy data with normalization errors.
Commonly employed data models for maximum likelihood refinement of electron microscopy images behave poorly in the presence of normalization errors. Small variations in background mean or signal brightness are relatively common in cryo-electron microscopy data, and varying signal-to-noise ratios or artifacts in the images interfere with standard normalization procedures. In this paper, a statistical data model that accounts for normalization errors is presented, and a corresponding algorithm for maximum likelihood classification of structurally heterogeneous projection data is derived. The extended data model has general relevance, since similar algorithms may be derived for other maximum likelihood approaches in the field. The potentials of this approach are illustrated for two structurally heterogeneous data sets: 70S E.coli ribosomes and human RNA polymerase II complexes. In both cases, maximum likelihood classification based on the conventional data model failed, whereas the new approach was capable of revealing previously unobserved conformations.
Scheres
Sjors H W
SH
Centro Nacional de Biotecnología-CSIC, Calle Darwin 3, Campus Universidad Autonoma, Cantoblanco, 28049 Madrid, Spain. scheres@cnb.csic.es
Valle
Mikel
M
Grob
Patricia
P
Nogales
Eva
E
Carazo
José-María
JM
D000465:Algorithms; D004926:Escherichia coli / Q000648:ultrastructure; D006801:Humans; D016013:Likelihood Functions; D008854:Microscopy, Electron / Q000379:methods*; D008962:Models, Theoretical; D012319:RNA Polymerase II / Q000648:ultrastructure; D012270:Ribosomes / Q000648:ultrastructure
D012319:RNA Polymerase II