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Motif Search in Electron Tomography

  • Achilleas S. Frangakis
  • Bimal K. Rath

Abstract

Cryoelectron tomography aims to act as an interface between two levels of 3D imaging: in vivo cell imaging and techniques achieving atomic resolution (e.g., X-ray crystallography). This most likely will happen through a computational motif search by mapping structures with atomic resolution into lower-resolution tomograms of cells and organelles. There exist a large variety of pattern recognition techniques in engineering, which can perform different types of motif search. This chapter will focus on cross-correlation techniques, which aim to identify a motif within a noisy 3D image (the tomogram or the 3D reconstruction). Generally, the success of the crosscorrelation approach depends on the resolution of the tomograms, the degree of corruption of the motif by noise as well as the fidelity with which the template matches the motif. For maximal detection signal, the template should have the same impulse response as the motif, which in this case is the macromolecule sought. Since the noise in the tomogram cannot be significantly decreased after data recording, the task of designing an accurate template reduces to the determination of the precise parameters of the image recording conditions, so that the searched motifs may be modeled as accurately as possible.

Keywords

Eulerian Angle Ryanodine Receptor Pattern Recognition Technique Motif Search Cryoelectron Tomography 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Böhm, J., Frangakis, A. S., Hegerl, R., Nickell, S., Typke, D. and Baumeister, W. (2000). Toward detecting and identifying macromolecules in a cellular context: template matching applied to electron tomograms. Proc. Natl Acad. Sci. USA 97:14245–14250.PubMedCrossRefGoogle Scholar
  2. Cong, Y., Kovacs, J. A. and Wriggers, W. (2003). 2D fast rotational matching for image processing of biophysical data. J. Struct. Biol. 144:51–60.PubMedCrossRefGoogle Scholar
  3. Frangakis, A. S., Böhm, J., Forster, F., Nickell, S., Nicastro, D., Typke, D., Hegerl, R. and Baumeister, W. (2002). Identification of macromolecular complexes in cryoelectron tomograms of phantom cells. Proc. Natl. Acad. Sci. USA 99:14153–14158.PubMedCrossRefGoogle Scholar
  4. Kovacs, J.A., Chacon, P., Cong, Y., Metwally, E. and Wriggers, W. (2003). Fast rotational matching of rigid bodies by fast Fourier transform acceleration of five degrees of freedom. Acta Crystallogr. D Biol. Crystallogr. 59:1371–1376.PubMedCrossRefGoogle Scholar
  5. Pavelcik, F., Zelinka, J. and Otwinowski, Z. (2002). Methodology and applications of automatic electron-density map interpretation by six-dimensional rotational and translational search for molecular fragments. Acta Crystallogr. D Biol. Crystallogr. 58:275–283.PubMedCrossRefGoogle Scholar
  6. Rath, B. K., Hegerl, R., Leith, A., Shaikh, T. R., Wagenknecht, T. and Frank, J. (2003). Fast 3D motif search of EM density maps using a locally normalized cross-correlation function. J. Struct. Biol. 144:95–103.PubMedCrossRefGoogle Scholar
  7. Roseman, A. M. (2003). Particle finding in electron micrographs using a fast local correlation algorithm. Ultramicroscopy 94: 225–236.PubMedCrossRefGoogle Scholar
  8. Roseman, A. M. (2004). FindEM-a fast, efficient program for automatic selection of particles from electron micrographs. J. Struct. Biol. 145:91–99.PubMedCrossRefGoogle Scholar
  9. Rosenthal, P. B. and Henderson, R. (2003). Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333:721–745.PubMedCrossRefGoogle Scholar
  10. Stewart, P. L., Fuller, S. D. and Burnett, R. M. (1993). Diference imaging of adenovivus: bridging the resolution gap between X-ray crystallography and electron microscopy. EMBO J. 12:2589–2599.PubMedGoogle Scholar
  11. Zhu, Y., Carragher, B., Glaeser, R. M., Fellman, D., Bajaj, C., Bern, M., Mouche, F., de Hass, F., Hall, R. J., Kriegman, D. J., Ludtke, S. J., Mallick, S. P., Penczek, P. A., Roseman, A. M., Sigworth, F. J., Volkmann, N. and Potter, C. S. (2004). Automatic particle selection: results of a comparative study. J. Struct. Biol. 145:3–14.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Achilleas S. Frangakis
    • 1
  • Bimal K. Rath
    • 2
  1. 1.EMBL, European Molecular Biology LaboratoryHeidelbergGermany
  2. 2.Wadsworth CenterEmpire State PlazaAlbanyUSA

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