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Journal Article

High-confidence 3D template matching for cryo-electron tomography

MPS-Authors
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Cruz-León,  Sergio       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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Majtner,  Tomáš       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;

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Hoffmann,  Patrick C.       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;

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Kreysing,  Jan Philipp       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
IMPRS-CBP, Max Planck Institute of Biophysics, Max Planck Society;

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Tuijtel,  Maarten       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;

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Schaefer,  Stefan L.       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;

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Geißler,  Katharina       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
IMPRS-CBP, Max Planck Institute of Biophysics, Max Planck Society;

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Beck,  Martin       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biochemistry, Goethe University Frankfurt, Frankfurt am Main, Germany;

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Turoňová,  Beata       
Department of Molecular Sociology, Max Planck Institute of Biophysics, Max Planck Society;

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Hummer,  Gerhard       
Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society;
Institute of Biophysics, Goethe University Frankfurt, Frankfurt am Main, Germany;

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s41467-024-47839-8.pdf
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Citation

Cruz-León, S., Majtner, T., Hoffmann, P. C., Kreysing, J. P., Kehl, S., Tuijtel, M., et al. (2024). High-confidence 3D template matching for cryo-electron tomography. Nature Communications, 15: 3992. doi:10.1038/s41467-024-47839-8.


Cite as: https://hdl.handle.net/21.11116/0000-000F-4B02-B
Abstract
Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.