Maximum-likelihood Multi-reference Refinement for Electron Microscopy Images

A maximum-likelihood approach to multi-reference image refinement is presented. In contrast to conventional cross-correlation refinement, the new approach includes a formal description of the noise, implying that it is especially suited to cases with low signal-to-noise ratios. Application of this a...

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Bibliographic Details
Published in:Journal of molecular biology 2005-04, Vol.348 (1), p.139-149
Main Authors: Scheres, Sjors H.W., Valle, Mikel, Nuñez, Rafael, Sorzano, Carlos O.S., Marabini, Roberto, Herman, Gabor T., Carazo, Jose-Maria
Format: Article
Language:English
Subjects:
2D-alignment
Algorithms
Animals
Antigens, Polyomavirus Transforming - chemistry
Antigens, Polyomavirus Transforming - metabolism
Antigens, Polyomavirus Transforming - ultrastructure
classification
Cryoelectron Microscopy - methods
Likelihood Functions
Mathematics
maximum-likelihood
multi-reference refinement
Replication Origin
Simian virus 40
single-particles
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Summary:A maximum-likelihood approach to multi-reference image refinement is presented. In contrast to conventional cross-correlation refinement, the new approach includes a formal description of the noise, implying that it is especially suited to cases with low signal-to-noise ratios. Application of this approach to a cryo-electron microscopy dataset revealed two major classes for projections of simian virus 40 large T-antigen in complex with an asymmetric DNA-probe, containing the origin of simian virus 40 replication. Strongly bent projections of dodecamers showed density that may be attributed to the complexed double-stranded DNA, while almost straight projections revealed a twist in the relative orientation of the hexameric subunits. This new level of detail for large T-antigen projections was not detected using conventional techniques. For a negative stain dataset, maximum-likelihood refinement yielded results that were practically identical to those obtained using conventional multi-reference refinement. Results obtained using simulated data suggest that the efficiency of the maximum-likelihood approach may be further enhanced by explicitly incorporating the microscope contrast transfer function in the image formation model.
ISSN:0022-2836
1089-8638
DOI:10.1016/j.jmb.2005.02.031