EMPIAR-13108 - FusA (Pectobacterium atrosepticum ferrodoxin receptor) in the presence of Ra-LPS [7594 multi-frame micrographs composed of 40 frames each in MRC format]

EMPIAR-13108

FusA (Pectobacterium atrosepticum ferrodoxin receptor) in the presence of Ra-LPS [7594 multi-frame micrographs composed of 40 frames each in MRC format]

Publication:

The Structure of the bacterial outer membrane transporter FusA enabled by addition of the native lipid lipopolysaccharide

Machin JM, Khedidja M, Prakaash D, Radford SE, Walker D, Kalli A, Ranson NA

Journal of Structural Biology: X (---)

Related EMDB entry:

EMD-53974

Deposited:

2025-11-17

Released:

2025-12-16

Last modified:

2025-12-16

Imageset size:

1.09 TB

Imageset DOI:

10.6019/EMPIAR-13108

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned movies for P.atrosepticum FusA:Ra-LPS

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

7594

Image size:

(4096, 4096)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.91, 0.91)

Details:

7594 micrographs of 40 frame movies. Per-frame dose of 0.92 e/A2. Total dose of 37.07 e/A2. Exposure time of 7.24s. Originally collected as EER files and converted to TIF frames.

Files:

このエントリはPDBjからダウンロードすることはできません。

選択したファイルを無圧縮zipファイルにアーカイブ化してダウンロードします。

ダウンロードするファイル数やサイズによっては、膨大な時間がかかります。また、この機能は不安定で正常に動作しない可能性があり、ダウンロードされたファイルのチェックサムは検証されません。

rsync、aspera、globus 等の利用をおすすめします。

選択したファイルのリストをダウンロードします。

rsyncコマンド等でファイルリストを指定してダウンロードすることが可能です。

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

Browse All Files

Quick Links

Browse EMPIAR

Sample Preparation Widget

Volume Browser

Tomo Viewer Prototype

Claim entries to your ORCiD

Talks and Tutorials

EMPIAR Quick tour

Publications

Re-use study

Statistics

EMPIAR in the news

COVID-19 Data Portal

EMDB

BioImage Archive

PDBe

EMNavigator@PDBj

PDBj

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)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (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)

Dual site targeting of the bacterial 70S ribosome by tetracyclines.

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (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)