EMPIAR-10536 - Single particle cryo-EM dataset for MlaFEDB from E. coli in nanodisc [multiple data sets in MRC and MRCS formats]

EMPIAR-10536

Single particle cryo-EM dataset for MlaFEDB from E. coli in nanodisc [multiple data sets in MRC and MRCS formats]

Publication:

Structure of bacterial phospholipid transporter MlaFEDB with substrate bound

Coudray N, Isom GL, MacRae M, Saiduddin MN, Bhabha G, Ekiert DC

eLife 9 (2020)

PMID: 33236984

DOI: 10.7554/elife.62518

Related EMDB entry:

EMD-22116

Deposited:

2020-06-10

Released:

2020-12-09

Last modified:

2021-01-29

Imageset size:

996.26 GB

Imageset DOI:

10.6019/EMPIAR-10536

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
picked particles - single frame - processed

Image Sets

1. Unaligned multi-frame micrographs of MlaFEDB complex

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

3212

Image size:

(7420, 7676)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.416, 0.416)

Details:

Unaligned super-resolution (K2 camera, Titan Krios) multi-frame micrographs (total electron dose: 71 e-/A2; frame rate: 200 ms)

Files:

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2. Polished particles (using Relion 3.1)

Category:

picked particles - single frame - processed

Image format:

MRCS

No. of images or tilt series:

3064

Image size:

(500, 500)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.832, 0.832)

Details:

Picked particles after polishing in Relion 3.1.

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)