EMPIAR-11653 - Single particle cryo-EM dataset of bovine complex I in the deactive state [2988 multi-frame micrographs composed of 20 frames each in MRC format]

EMPIAR-11653

Single particle cryo-EM dataset of bovine complex I in the deactive state [2988 multi-frame micrographs composed of 20 frames each in MRC format]

Publication:

Structure of the Deactive State of Mammalian Respiratory Complex I

Blaza JN, Vinothkumar KR, Hirst J

Structure (London, England : 1993) 26 (2018) 312-319000.0

PMID: 29395787

DOI: 10.1016/j.str.2017.12.014

Related EMDB entry:

EMD-3731

Deposited:

2023-08-10

Released:

2023-10-03

Last modified:

2023-10-03

Imageset size:

965.82 GB

Imageset DOI:

10.6019/EMPIAR-11653

Experimental metadata:

Download xml json

Compressed by PDBj:

Image formats and pixel types shown below are those of the original, non-compressed files.

--- file not found ---

Contains:

micrographs - multiframe

Image Sets

1. Unaligned, gain-corrected micrographs of mitochondrial complex I from Bos taurus in the deactive state

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

2988

Image size:

(4096, 4096)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(1.38, 1.38)

Details:

The final particle positions are in columns 11 (X) and 12 (Y) of the shiny.star file. Micrographs are the average of 20 unaligned, gain-corrected frames. Data were collected over 2.5 seconds with a dose rate of 32 e / A-2 / s with a total dose of 80 e/A-2.

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Citations

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

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Deng Y, Wang S, Xiang M, Li Y, Zhuo L, Cao D, Fu X, Zou Q. (2026)

Chart builder: an interactive tool for user driven data visualization in the electron microscopy data bank.

Fonseca N, Duraisamy AK, Wang Z, Somasundharam S, Tayebinia M, de Oliveira LC, Ma M, Turner J, Patwardhan A, Kleywegt GJ, Hartley M, Morris KL. (2026)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements.

Jones HN, Deshmukh A, Pande K. (2026)

CryoGS: High-Quality Cryo-EM Homogeneous Reconstruction by Gaussian Splatting

Zeng B, Liu S, Cheng S, Xu G, Fan H. (2026)

Point cloud deformation modeling for particle selection following cryo-EM 2D classification.

Wang X, Mo Z, Li F, Zhang F, Wan X. (2026)

ICECREAM: high-fidelity equivariant cryo-electron tomography.

Kishore V, Debarnot V, Righetto RD, Engel BD, Dokmanić I. (2026)

CryoSift: an accessible and automated CNN-driven tool for cryo-EM 2D class selection.

Schäfer JH, Calza A, Hom K, Damodar P, Peng R, Bogdanović N, Lander GC, Stagg SM, Cianfrocco MA. (2025)