EMD-13093

Single-particle
4.52 Å
EMD-13093 Deposition: 16/06/2021
Map released: 02/02/2022
Last modified: 20/04/2022
Overview 3D View Sample Experiment Validation Volume Browser Additional data Links
Overview 3D View Sample Experiment Validation Volume Browser Additional data Links

EMD-13093

Immature 60S Ribosomal Subunit from C. thermophilum

EMD-13093

Single-particle
4.52 Å
EMD-13093 Deposition: 16/06/2021
Map released: 02/02/2022
Last modified: 20/04/2022
Overview 3D View Sample Experiment Validation Volume Browser Additional data Links
Sample Organism: Chaetomium thermophilum var. thermophilum DSM 1495
Sample: Native 60-mer core of Pyruvate Dehydrogenase Complex
Raw data: EMPIAR-10892

Deposition Authors: Skalidis I , Kastritis PL
Cryo-EM and artificial intelligence visualize endogenous protein community members.
Skalidis I , Kyrilis FL , Tuting C, Hamdi F , Chojnowski G , Kastritis PL
(2022) Structure , 30 , 575 - 589.e6
PUBMED: 35093201
DOI: doi:10.1016/j.str.2022.01.001
ISSN: 0969-2126
ASTM: STRUE6
Abstract:
Cellular function is underlined by megadalton assemblies organizing in proximity, forming communities. Metabolons are protein communities involving metabolic pathways such as protein, fatty acid, and thioesters of coenzyme-A synthesis. Metabolons are highly heterogeneous due to their function, making their analysis particularly challenging. Here, we simultaneously characterize metabolon-embedded architectures of a 60S pre-ribosome, fatty acid synthase, and pyruvate/oxoglutarate dehydrogenase complex E2 cores de novo. Cryo-electron microscopy (cryo-EM) 3D reconstructions are resolved at 3.84-4.52 Å resolution by collecting <3,000 micrographs of a single cellular fraction. After combining cryo-EM with artificial intelligence-based atomic modeling and de novo sequence identification methods, at this resolution range, polypeptide hydrogen bonding patterns are discernible. Residing molecular components resemble their purified counterparts from other eukaryotes but also exhibit substantial conformational variation with potential functional implications. Our results propose an integrated tool, boosted by machine learning, that opens doors for structural systems biology spearheaded by cryo-EM characterization of native cell extracts.