EMPIAR-12796 - CryoEM dataset of Retron-Eco1 E106A filaments [7748 multi-frame micrographs composed of 40 frames each in MRC format]

EMPIAR-12796

CryoEM dataset of Retron-Eco1 E106A filaments [7748 multi-frame micrographs composed of 40 frames each in MRC format]

Publication:

Retron-Eco1 assembles NAD⁺-hydrolyzing filaments that provide immunity against bacteriophages

Carabias A, Camara-Wilpert S, Mestre MR, Lopéz-Méndez B, Hendriks IA, Zhao R, Pape T, Fuglsang A, Luk SH, Nielsen ML, Pinilla-Redondo R, Montoya G

Molecular cell 84 (2024) 2185-2202000000000000.0

PMID: 38788717

DOI: 10.1016/j.molcel.2024.05.001

Related EMDB entry:

Deposited:

2025-05-26

Released:

2025-07-01

Last modified:

2025-07-01

Imageset size:

4.66 TB

Imageset DOI:

10.6019/EMPIAR-12796

Experimental metadata:

Download xml json

Compressed by PDBj:

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

--- file not found ---

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multiframe micrographs of Retron-Eco1 E106A mutant

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

7748

Image size:

(4096, 4096)

Pixel type:

UNSIGNED 16 BIT INTEGER

Pixel spacing:

(0.832, 0.832)

Details:

Movies were collected in a Titan Krios G2 equipped with a TFS Falcon III camera operated at 300 keV in counting mode at a magnification of x 96000 (0.832 A˚ /px) using a total dose of 41 e/A˚ 2. Spherical Aberration: 2.7 mm. Out of the original 7748 micrographs, 7250 were used for particle picking. Micrographs are gain-corrected, non-motion-corrected.

Microscope Settings:
-Acceleration Voltage: 300 kV
-Pixel Size: 0.832 Å
-Spherical Aberration: 2.7 mm
-Total Exposure Dose: 41 e/Å²
-Number of Fractions: 40

The images for every grid square are in different directories following the next architecture:

data/GM_artcar-acdr0052_86_Filament_20220720_094043/Images-Disc1/GridSquare_*/Data

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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)

Unbend, correction of local beam-induced sample motion in cryo-EM images using a 3D spline model.

Kong L, Zottig X, Elferich J, Grigorieff N. (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)

Dual site targeting of the bacterial 70S ribosome by tetracyclines.

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (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)