Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 166, Issue 2, Pages 234-240Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2009.02.007
Keywords
Single particle analysis; Structural heterogeneity; Classification; Expectation maximization
Funding
- Spanish Ministry of Science [CSD2006-00023, BIO2007-67150-C03-1/3]
- Comunidad de Madrid [S-GEN-0166-2006]
- European Union [FP6-502828]
- US National Heart, Lung and Blood Institute
- National Institutes of Health [R01 HL070472, R01 GM63072]
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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 11 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. (C) 2009 Elsevier Inc. All rights reserved.
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