EMPIAR-12865 - Rat GluN1-GluN2B NMDA receptor channel in complex with glycine [multiple data sets in TIFF format]

EMPIAR-12865

Rat GluN1-GluN2B NMDA receptor channel in complex with glycine [multiple data sets in TIFF format]

Publication:

Molecular Mechanism of ligand-gating and opening of NMDA receptor

Chou THC, Epstein ME, Fritzemeier RF, Akins NA, Paladugu SP, Ullman EU, Liotta DL, Traynelis SFT, Furukawa HF

632 (2024) 209-217

PMID: 39085540

DOI: 10.1038/s41586-024-07742-0

Related EMDB entry:

EMD-43782

Deposited:

2024-10-19

Released:

2025-07-25

Last modified:

2025-07-25

Imageset size:

1.22 TB

Imageset DOI:

10.6019/EMPIAR-12865

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned micrograph movies for Rat GluN1-GluN2B NMDA receptor channel in complex with glycine - dataset 1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

4649

Image size:

(5760, 3838)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.07, 1.07)

Details:

---

Files:

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2. Unaligned micrograph movies for Rat GluN1-GluN2B NMDA receptor channel in complex with glycine - dataset 2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

4290

Image size:

(5760, 3838)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.07, 1.07)

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

prismPYP: Power-spectrum and image domain learning for self-supervised micrograph evaluation.

He L, Bartesaghi A. (2026)

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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)