EMPIAR-13114 - Cryo-EM of connexin-46/50 in amphipol at low pH (Collection #2) [557 multi-frame micrographs composed of 50 frames each in MRC format]

EMPIAR-13114

Cryo-EM of connexin-46/50 in amphipol at low pH (Collection #2) [557 multi-frame micrographs composed of 50 frames each in MRC format]

Publication:

Reversible lipid-mediated pH-gating of connexin-46/50 by cryo-EM

Jarodsky JM, Myers JB, Reichow SL

Nature communications 17 (2026)

PMID: 41526355

DOI: 10.1038/s41467-026-68311-9

Related EMDB entry:

EMD-73876

Deposited:

2025-11-19

Released:

2026-04-07

Last modified:

2026-04-07

Imageset size:

237.66 GB

Imageset DOI:

10.6019/EMPIAR-13114

Experimental metadata:

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Compressed by PDBj:

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

Movies in this entry (data/*/BluJ-4_*.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:   1,545,708,702 kilobytes
Compressed size:   248,977,831 kilobytes

Contains:

micrographs - multiframe

Image Sets

1. Unaligned mutli-frame raw micrographs of Connexin-46/50 in amphipol at low pH

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

557

Image size:

(7676, 7420)

Pixel type:

SIGNED 16 BIT INTEGER

Pixel spacing:

(0.665, 0.665)

Details:

---

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Citations

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)

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

He L, Bartesaghi A. (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)