EMPIAR-10454 - Saccharomyces cerevisiae fatty acid synthase complex with bound gamma subunit [multiple data sets in MRC and MRCS formats]

EMPIAR-10454

Saccharomyces cerevisiae fatty acid synthase complex with bound gamma subunit [multiple data sets in MRC and MRCS formats]

Publication:

Discovery of a Regulatory Subunit of the Yeast Fatty Acid Synthase

Singh K, Graf B, Linden A, Sautner V, Urlaub H, Tittmann K, Stark H, Chari A

Cell 180 (2020) 1130-1143

PMID: 32160528

DOI: 10.1016/j.cell.2020.02.034

Related EMDB entry:

EMD-4577

Deposited:

2020-05-22

Released:

2020-07-27

Last modified:

2020-07-27

Imageset size:

329.30 GB

Imageset DOI:

10.6019/EMPIAR-10454

Experimental metadata:

Download xml json

Contains:

micrographs - single frame
picked particles - single frame - processed

Image Sets

1. Aligned and doseweighted micrographs of yeast fatty acid synthase with bound gamma subunit

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

4593

Image size:

(4096, 4096)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.06, 1.06)

Details:

---

Files:

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2. Stack of Polished particles (in relion) used for final reconstruction

Category:

picked particles - single frame - processed

Image format:

MRCS

No. of images or tilt series:

110597

Image size:

(320, 320)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(1.06, 1.06)

Details:

---

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)

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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)