EMPIAR-11163 - E. coli 50S ribosome bound to SLC31 [multiple data sets in MRC and TIFF formats]

EMPIAR-11163

E. coli 50S ribosome bound to SLC31 [multiple data sets in MRC and TIFF formats]

Publication:

SLC collection of antibiotic analogs

Seiple IB, Pellegrino J, Fraser JS, Lee DJ, Yeon SK

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Related EMDB entry:

EMD-27884

Deposited:

2022-08-07

Released:

2023-06-30

Last modified:

2023-06-30

Imageset size:

3.58 TB

Imageset DOI:

10.6019/EMPIAR-11163

Experimental metadata:

Download xml json

Contains:

micrographs - single frame
micrographs - multiframe

Image Sets

1. dose weighted micrographs

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

4632

Image size:

(5760, 4092)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.8261, 0.8261)

Details:

---

Files:

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2. unaligned movies

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

4632

Image size:

(5760, 4092)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.8261, 0.8261)

Details:

Includes Gain Reference file

Files:

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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Citations

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

He L, Bartesaghi A. (2026)

CryoSIP: unleashing protein high-resolution Cryo-EM via semantic-instance collaborative picking.

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)