EMPIAR-10997 - A drug and ATP binding site in type 1 ryanodine receptor [6890 multi-frame micrographs composed of 50 frames each in TIFF format]

EMPIAR-10997

A drug and ATP binding site in type 1 ryanodine receptor [6890 multi-frame micrographs composed of 50 frames each in TIFF format]

Publication:

A drug and ATP binding site in type 1 ryanodine receptor

Melville Z, Dridi H, Yuan Q, Reiken S, Wronska A, Liu Y, Clarke OB, Marks AR

Structure (2022)

Related EMDB entry:

EMD-26205

Deposited:

2022-04-04

Released:

2022-05-25

Last modified:

2022-05-25

Imageset size:

1.53 TB

Imageset DOI:

10.6019/EMPIAR-10997

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Type-1 ryanodine receptor

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

6890

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.831, 0.831)

Details:

Gain reference file:
data/20sep25b_25181344_01_4092x5760_norm_0.mrc

Files:

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

Depending on the number and size of files to be downloaded, this can take an enormous amount of time. Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.

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)

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)

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)