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

Autor: Cruz-León S; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Majtner T; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Hoffmann PC; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Kreysing JP; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany.; IMPRS on Cellular Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Kehl S; Max Planck Computing and Data Facility, Gießenbachstraße 2, 85748, Garching, Germany., Tuijtel MW; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Schaefer SL; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Geißler K; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany.; IMPRS on Cellular Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany., Beck M; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany. martin.beck@biophys.mpg.de.; Institute of Biochemistry, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany. martin.beck@biophys.mpg.de., Turoňová B; Department of Molecular Sociology, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany. beata.turonova@biophys.mpg.de., Hummer G; Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt am Main, Germany. gerhard.hummer@biophys.mpg.de.; Institute of Biophysics, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany. gerhard.hummer@biophys.mpg.de.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 May 11; Vol. 15 (1), pp. 3992. Date of Electronic Publication: 2024 May 11.
DOI: 10.1038/s41467-024-47839-8
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
Externí odkaz: