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A method for the alignment of heterogeneous macromolecules from electron microscopy.

ABSTRACT: We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far less model-dependent bias than is found with cross-correlation based alignment. We also demonstrate that MI improves the alignment of some types of heterogeneous data, provided that the signal-to-noise ratio is relatively high. These results indicate, therefore, that use of MI as the scoring function is well suited for the alignment of class-averages computed from single-particle images. Our method is tested on data from three model structures and one real dataset.

SUBMITTER: Shatsky M

PROVIDER: S-EPMC2740748 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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A method for the alignment of heterogeneous macromolecules from electron microscopy.

Shatsky Maxim M Hall Richard J RJ Brenner Steven E SE Glaeser Robert M RM

Journal of structural biology 20081230 1

We propose a feature-based image alignment method for single-particle electron microscopy that is able to accommodate various similarity scoring functions while efficiently sampling the two-dimensional transformational space. We use this image alignment method to evaluate the performance of a scoring function that is based on the Mutual Information (MI) of two images rather than one that is based on the cross-correlation function. We show that alignment using MI for the scoring function has far ...[more]

PMID: 19166941

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