EMPIAR-13446 - Cryo-EM structure of the IS621 recombinase in complex with bridge RNA, left-half DNA, and right-half DNA [multiple data sets in TIFF format]

EMPIAR-13446

Cryo-EM structure of the IS621 recombinase in complex with bridge RNA, left-half DNA, and right-half DNA [multiple data sets in TIFF format]

Publication:

Structural mechanism governing the directionality of bridge recombination

Hiraizumi M, Athukoralage JS, Perry NT, Tsujimoto E, Shiojiri N, Nagahata N, Lee L, Sun G, Durrant MG, Chandrasekaran SS, Konermann S, Yamashita K, Hsu PD, Nishimasu H

Nature (2026)

PMID: 42587149

DOI: 10.1038/s41586-026-10903-y

Related EMDB entry:

EMD-65979 EMD-65978

Deposited:

2026-03-28

Released:

2026-08-24

Last modified:

2026-08-24

Imageset size:

3.21 TB

Imageset DOI:

10.6019/EMPIAR-13446

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. EMD-65978 (pre)

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

6558

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

Gain: data_1/GridSquare_26880948/Data/FoilHole_26915916_Data_26915121_26915123_20231226_165524_gain.tiff

Files:

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2. EMD-65979 (post)

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

7668

Image size:

(5760, 4092)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.83, 0.83)

Details:

data_1 and data_2 were collected from different grids.
gain: data_1/GridSquare_24969694/Data/FoilHole_25057155_Data_25055102_25055104_20240125_225422_gain.tiff

Files:

This entry cannot be downloaded from PDBj.

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We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

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Citations

A variational framework with composite sparse regularization for cryo-electron tomography reconstruction.

Yu C, Xu Z, Zeng Q, Wan X, El-Messiry H, Zhang F, Han R. (2026)

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Poudel B, Gyawali R, Dhakal A, Cheng J, Xu D. (2026)

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)

CryoVirusDB: An Annotated Dataset for AI-Based Virus Particle Identification in Cryo-EM Micrographs.

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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)

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)

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

Devarkar SC, Lomakin IB, Wang J, Grada A, Bunick CG. (2026)

The vault associates with membranes in situ.

Geißler K, Kreysing JP, Wang Y, Glushkova D, Obarska-Kosinska A, Hoffmann PC, Böhm S, Schmidt A, Meier-Credo J, Langer JD, Hummer G, Beck M. (2026)

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