EMPIAR-10913 - Cryo-EM micrographs of GABA(A)Rs purified from cells expressing human full-length alpha4, beta3 and delta subunits, in presence of HEPES and nanobody Nb25 [18161 multi-frame micrographs composed of 32 frames each in TIFF format]

EMPIAR-10913

Cryo-EM micrographs of GABA(A)Rs purified from cells expressing human full-length alpha4, beta3 and delta subunits, in presence of HEPES and nanobody Nb25 [18161 multi-frame micrographs composed of 32 frames each in TIFF format]

Publication:

Differential assembly diversifies GABA_A receptor structures and signalling

Chirgadze DY, Sente A, Miller KW, Desai R, Aricescu AR, Naydenova K, Malinauskas T, Jounaidi Y, Miehling J, Zhou X, Masiulis S, Hardwick SW

Nature (2022)

PMID: 35355020

DOI: 10.1038/s41586-022-04517-3

Related EMDB entry:

EMD-14068 EMD-14067

Deposited:

2021-12-29

Released:

2022-04-04

Last modified:

2022-04-04

Imageset size:

4.15 TB

Imageset DOI:

10.6019/EMPIAR-10913

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multi-frame micrographs of GABA(A) receptors purified from cells expressing alpha4, beta3 and delta subunits, in presence of HEPES and nanobody Nb25

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

18161

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.325, 0.325)

Details:

K3 super-resolution movies with a total dose of 40 e/A2 and exposure time of 1.31 s.
The gain reference used for data processing is given in data/superres_gain_from_epu.mrc. In RELION:
Gain rotaion: 180 degrees (2)
Gain flip: flip left to right (2)

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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)