EMPIAR-13726 - JMV449-NTSR1-Gi in 15 seconds following the GDP addition [multiple data sets in TIFF format]

EMPIAR-13726

JMV449-NTSR1-Gi in 15 seconds following the GDP addition [multiple data sets in TIFF format]

Publication:

The dynamic basis of G-protein recognition and activation by a GPCR

Kobayashi K, Kawakami K, Matsui TE, Yokoi S, Fukuda M, Narita TJ, Arai H, Tambo M, Sumikama T, Tatsumi M, Yamashita K, Koyanagi J, Kugawa M, Ikeda H, Sumino A, Mitsutake A, Kobilka BK, Inoue A, Kato HE

(---)

DOI: 10.1038/s41586-026-10228-w

Related EMDB entry:

EMD-64916 EMD-64915

Deposited:

2026-02-19

Released:

2026-08-04

Last modified:

2026-08-04

Imageset size:

3.30 TB

Imageset DOI:

10.6019/EMPIAR-13726

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. imageset_1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

6416

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

- Please ignore the pixel size in the header (1.00 Å/px); use 0.83 Å/px.
- Cs: 2.7mm
- HT: 300 kV
- Dose per frame: 1 electron/A^2/frame
- Please use "movie_gain.mrc" as a gain reference with flipping along Y for CryoSPARC

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2. imageset_2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

8074

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

Files:

This entry cannot be downloaded from PDBj.

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

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Yu C, Xu Z, Zeng Q, Wan X, El-Messiry H, Zhang F, Han R. (2026)

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Poudel B, Gyawali R, Dhakal A, Cheng J, Xu D. (2026)

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)

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)

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

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (2026)

The vault associates with membranes in situ.

Geißler K, Kreysing JP, Wang Y, Glushkova D, Obarska-Kosinska A, Hoffmann PC, Böhm S, Schmidt A, Meier-Credo J, Langer JD, Hummer G, Beck M. (2026)

See all citations

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[2026-08-05]

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