High-confidence 3D template matching for cryo-electron tomography.
Nat Commun
; 15(1): 3992, 2024 May 11.
Article
em En
| MEDLINE
| ID: mdl-38734767
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ribossomos
/
Software
/
Microscopia Crioeletrônica
/
Proteômica
/
Complexo de Endopeptidases do Proteassoma
/
Tomografia com Microscopia Eletrônica
Limite:
Humans
Idioma:
En
Revista:
Nat Commun
Ano de publicação:
2024
Tipo de documento:
Article