EMPIAR-12799 - CryoEM dataset of Retron-Eco1 "-1-turn" mutant filaments in the presence of NAD+ [28871 multi-frame micrographs composed of 40 frames each in EER format]

EMPIAR-12799

CryoEM dataset of Retron-Eco1 "-1-turn" mutant filaments in the presence of NAD+ [28871 multi-frame micrographs composed of 40 frames each in EER 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:

EMD-19792

Deposited:

2025-05-26

Released:

2025-06-30

Last modified:

2025-06-30

Imageset size:

12.77 TB

Imageset DOI:

10.6019/EMPIAR-12799

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. Unaligned multiframe micrographs of Retron-Eco1 "-1 turn" mutant

Category:

micrographs - multiframe

Image format:

EER

No. of images or tilt series:

28871

Image size:

(4096, 4096)

Pixel type:

UNSIGNED BYTE

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 165000 (0.725 A˚ /px), using a total dose of 50 e/A˚ 2. Spherical Aberration: 2.7 mm. Out of the original 28871 micrographs, 26559 were used for particle picking. Micrographs are non gain-corrected, non-motion-corrected. The gain reference file is located in the Data folder, file: "20231221_154105_EER_GainReference.gain". Up sampling factor: 1 was used for the processing in CryoSPARC.

Microscope Settings:
-Acceleration Voltage: 300 kV
-Pixel Size: 0.725 Å
-Spherical Aberration: 2.7 mm
-Total Exposure Dose: 50 e/Å²

Files:

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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)