EMPIAR-10996 - Endogenous ligand recognition and structural transition of a human PTH receptor. [multiple data sets in TIFF format]

EMPIAR-10996

Endogenous ligand recognition and structural transition of a human PTH receptor. [multiple data sets in TIFF format]

Publication:

Endogenous ligand recognition and structural transition of a human PTH receptor.

Kobayashi K, Kawakami K, Kusakizako T, Miyauchi H, Tomita A, Kobayashi K, Shihoya W, Yamashita K, Nishizawa T, Kato HE, Inoue A, Nureki O

(---)

Related EMDB entry:

EMD-32141 EMD-32146 EMD-32143 EMD-32144 EMD-32142 EMD-32145

Deposited:

2022-02-17

Released:

2022-10-07

Last modified:

2022-10-07

Imageset size:

3.78 TB

Imageset DOI:

10.6019/EMPIAR-10996

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned movies for the PTH bound human PTH1R-Gs complex

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

6205

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

Gain reference is provided, which need to be flipped (‘Flip upside down (1)’ in RELION).

Files:

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2. Unaligned movies for the PTHrP bound human PTH1R-Gs complex

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

9405

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

Gain reference is provided, which need to be flipped (‘Flip upside down (1)’ in RELION).

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)