EMPIAR-10669 - Cryo-EM dataset of the substrate-engaged human 26S proteasome [44688 micrographs in MRC format]

EMPIAR-10669

Cryo-EM dataset of the substrate-engaged human 26S proteasome [44688 micrographs in MRC format]

Publication:

Cryo-EM structures and dynamics of substrate-engaged human 26S proteasome

Dong Y, Zhang S, Wu Z, Li X, Wang WL, Zhu Y, Stoilova-McPhie S, Lu Y, Finley D, Mao Y

Nature 565 (2018) 49-55

PMID: 30479383

DOI: 10.1038/s41586-018-0736-4

Related EMDB entry:

EMD-9217 EMD-9216 EMD-9219 EMD-9221 EMD-9218 EMD-9222 EMD-9220 EMD-9225 EMD-9223 EMD-9227 EMD-9215 EMD-9224 EMD-9226 EMD-9228 EMD-9229

Deposited:

2020-12-23

Released:

2021-11-30

Last modified:

2021-11-30

Imageset size:

15.34 TB

Imageset DOI:

10.6019/EMPIAR-10669

Experimental metadata:

Download xml json

Contains:

micrographs - single frame

Image Sets

1. Drift-corrected frame-averaged super-counting mode micrographs and extracted particles of substrate-engaged human 26S proteasome

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

44688

Image size:

(7676, 7420)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.685, 0.685)

Details:

The micrographs were the drift-corrected averages of 39 raw move frames, accumulating 44 electron per squared Angstrom in a 10 second exposure, which were taken under super-counting mode of K2 Summit camera. The original movie contains 40 frames, with the first frame not being used after drift correction for the averaged micrographs. The original particle set picked and selected with removal of apparent junks is specified in data/particles/26S-original.star, and the remaining E*.star files specify the final homogeneous datasets of RP-CP states classified by AlphaCryo4D.

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Citations

GTpick: A deep neural network for Cryo-EM particle detection.

Ni S, Yang C, Liu Y, Zhang Y, Shi Y, Qian A, Kong R, Chang S. (2025)

High-resolution ab initio reconstruction enables cryo-EM structure determination of small particles

Kim K, Li H, Clarke OB. (2025)

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.

Krentzel D, Elphick M, Domart MC, Peddie CJ, Laine RF, Shand C, Henriques R, Collinson LM, Jones ML. (2025)

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.

Arora S, Pan SH, Kumar S, Singh SK, Chauhan U, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Chui KT, Gupta BB. (2025)

PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modeling and machine learning.

Costa-Gomes B, Greer J, Juraschko N, Parkhurst J, Mirecka J, Famili M, Rangel-Smith C, Strickson O, Lowe A, Basham M, Burnley T. (2025)

Self-supervised learning for generalizable particle picking in cryo-EM micrographs.

Zamanos A, Koromilas P, Bouritsas G, Kastritis PL, Panagakis Y. (2025)

Conserved hydrophilic checkpoints tune FocA-mediated formate:H<sup>+</sup> symport.

Tüting C, Janson K, Kammel M, Ihling C, Lorenz J, Kyrilis FL, Hamdi F, Erdmann C, Sinz A, Sawers RG, Kastritis PL. (2025)

Laser flash melting cryo-EM samples to overcome preferred orientation.

Straub MS, Harder OF, Mowry NJ, Barrass SV, Hruby J, Drabbels M, Lorenz UJ. (2025)

Icecream: High-Fidelity Equivariant Cryo-Electron Tomography

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2025)

In situ structure determination of conformationally flexible targets using nextPYP.

Liu HF, Zhou Y, Huang Q, Martin J, Bartesaghi A. (2025)

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