EMPIAR-13086 - GLIC in MSP1E3D1 nanodisc at pH 7.5 (asolectin) [5188 multi-frame micrographs composed of 40 frames each in MRC format]

EMPIAR-13086

GLIC in MSP1E3D1 nanodisc at pH 7.5 (asolectin) [5188 multi-frame micrographs composed of 40 frames each in MRC format]

Publication:

Cryo-EM structures of prokaryotic ligand-gated ion channel GLIC provide insights into gating in a lipid environment

Bharambe N, Li Z, Seiferth D, Balakrishna AM, Biggin PC, Basak S

Nature Communications 15 (2024)

PMID: 38580666

DOI: 10.1038/s41467-024-47370-w

Related EMDB entry:

EMD-35161

Deposited:

2025-01-08

Released:

2025-11-18

Last modified:

2025-11-18

Imageset size:

1.53 TB

Imageset DOI:

10.6019/EMPIAR-13086

Experimental metadata:

Download xml json

Compressed by PDBj:

Image formats and pixel types shown below are those of the original, non-compressed files.

Movies in this entry (data/movies/FoilHole_*.mrc) were compressed to TIFF by EMPIAR Japan to save space and bandwidth.
The compression is lossless in that all pixel intensities remain exactly the same after decompression.
However, some metadata in the MRC header were lost.
The original files before compression are available from EMPIAR EBI.

Original size:   11,542,480,300 kilobytes
Compressed size:  1,627,471,789 kilobytes

Contains:

micrographs - multiframe

Image Sets

1. GLIC in MSP1E3D1 nanodisc at pH 7.5 (asolectin)

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

5188

Image size:

(7676, 7420)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(1.064, 1.064)

Details:

Raw movies collected from Titan Krios equipped with K2 camera at 130kx magnification, pixel size 1.064ang/pix, total dose 40e.

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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)