High-throughput subtomogram alignment and classification by Fourier space constrained fast volumetric matching
Cryo-electron tomography allows the visualization of macromolecular complexes in their cellular environments in close-to-live conditions. The nominal resolution of subtomograms can be significantly increased when individual subtomograms of the same kind are aligned and averaged. A vital step for suc...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
May 2012
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| In: |
Journal of structural biology
Year: 2012, Jahrgang: 178, Heft: 2, Pages: 150-160 |
| ISSN: | 1095-8657 |
| DOI: | 10.1016/j.jsb.2012.02.014 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1016/j.jsb.2012.02.014 Verlag, Volltext: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3821800/ 
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| Verfasserangaben: | Min Xu, Martin Beck, and Frank Alber |
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| 520 | |a Cryo-electron tomography allows the visualization of macromolecular complexes in their cellular environments in close-to-live conditions. The nominal resolution of subtomograms can be significantly increased when individual subtomograms of the same kind are aligned and averaged. A vital step for such a procedure are algorithms that speedup subtomogram alignment and improve accuracy for reference-free subtomogram classification, which will facilitate automation of tomography analysis and overall high throughput in the data processing. In this paper, we propose a fast rotational alignment method that uses the Fourier equivalent form of a popular constrained correlation measure that considers missing wedge corrections and density variances in the subtomograms. The fast rotational search is based on 3D volumetric matching, which significantly improves the rotational alignment accuracy in particular for highly distorted subtomograms with low SNR and tilt angle ranges in comparison to a fast rotational alignment based on matching of projected 2D spherical images. We further integrate our fast rotational alignment method in a reference free iterative subtomogram classification scheme, and propose a local feature enhancement strategy in the classification process. We can demonstrate that the automatic method can be used to successfully classify a large number of experimental subtomograms without the need of a reference structure. | ||
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