EMPIAR-12559 - CryoEM structure of cyclised H-pilus [multiple data sets in TIFF format]

EMPIAR-12559

CryoEM structure of cyclised H-pilus [multiple data sets in TIFF format]

Publication:

Cryo-EM structure of the conjugation H-pilus reveals the cyclic nature of the TrhA pilin

Ishimoto N, Beis K

(---)

Related EMDB entry:

EMD-52431

Deposited:

2024-12-27

Released:

2025-04-09

Last modified:

2025-04-09

Imageset size:

112.77 GB

Imageset DOI:

10.6019/EMPIAR-12559

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multi-frame micrographs of H-pilus Data 1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

697

Image size:

(4096, 4096)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.921, 0.921)

Details:

---

Files:

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2. Unaligned multi-frame micrographs of H-pilus Data 2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

172

Image size:

(4096, 4096)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.921, 0.921)

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)

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)