EMPIAR-13564 - Engineered nanobody 1G1 bound to the acidic patch of the nucleosome [multiple data sets in TIFF and MRC formats]

EMPIAR-13564

Engineered nanobody 1G1 bound to the acidic patch of the nucleosome [multiple data sets in TIFF and MRC formats]

Publication:

Different modes of engagement with the nucleosome acidic patch yield distinct functional outcomes

Chakraborty U, Saccone EC, Becerra GC, Khan LF, Arslanovic N, Aguilar R, Gloor SL, Hunt SR, Folkwein HJ, Husby NL, Maier KE, Marunde MR, Schomburg NK, Vaidya A, Cowles MW, Venters BJ, Kassavetis G, Sun ZW, Kadonaga JT, Armache JP, Keogh MC, Tyler JK

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Related EMDB entry:

EMD-74114

Deposited:

2026-05-16

Released:

2026-06-10

Last modified:

2026-06-10

Imageset size:

3.96 TB

Imageset DOI:

10.6019/EMPIAR-13564

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Unaligned multi-frame images of the nucleosome-bound nanobody 1G1 bound to the nucleosome

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

8302

Image size:

(11520, 8184)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.549, 0.549)

Details:

---

Files:

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2. Motion-corrected and dose-weighted summed micrographs

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

8302

Image size:

(5760, 4092)

Pixel type:

UNSIGNED 32 BIT INTEGER

Pixel spacing:

(1.098, 1.098)

Details:

---

Files:

This entry cannot be downloaded from PDBj.

Archives and downloads the selected files into an uncompressed zip file.

We recommend using rsync, aspera, globus, etc.

Download a list of selected files.

It is possible to download files by specifying the file list with rsync command, etc.

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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)

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

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

He L, Bartesaghi A. (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)

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

Gyawali R, Dhakal A, Wang L, Cheng J. (2026)

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

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

See all citations

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