EMPIAR-12772 - Progesterone-bound DB3 Fab in complex with computationally designed DBPro1156_2 protein binder [multiple data sets in MRC format]

EMPIAR-12772

Progesterone-bound DB3 Fab in complex with computationally designed DBPro1156_2 protein binder [multiple data sets in MRC format]

Publication:

Targeting protein-ligand neosurfaces with a generalizable deep learning tool

Marchand A, Buckley S, Schneuing A, Pacesa M, Elia M, Gainza P, Elizarova E, Neeser RM, Lee PW, Reymond L, Miao Y, Scheller L, Georgeon S, Schmidt J, Schwaller P, Maerkl SJ, Bronstein M, Correia BE

Nature (2025)

PMID: 39814890

DOI: 10.1038/s41586-024-08435-4

Related EMDB entry:

EMD-50522

Deposited:

2025-02-11

Released:

2025-10-29

Last modified:

2025-10-29

Imageset size:

14.63 TB

Imageset DOI:

10.6019/EMPIAR-12772

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe
micrographs - single frame

Image Sets

1. Unaligned multi-frame movies of Progesterone-bound DB3 Fab in complex with computationally designed DBPro1156_2 protein binder

Category:

micrographs - multiframe

Image format:

MRC

No. of images or tilt series:

34507

Image size:

(4096, 4096)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.658, 0.658)

Details:

---

Files:

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2. Aligned dose-weighted micrographs of Progesterone-bound DB3 Fab in complex with computationally designed DBPro1156_2 protein binder

Category:

micrographs - single frame

Image format:

MRC

No. of images or tilt series:

34507

Image size:

(4096, 4096)

Pixel type:

32 BIT FLOAT

Pixel spacing:

(0.658, 0.658)

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.

rsync -av --files-from=(path-to-filelist) rsync://empiar.pdbj.org/empiar/archive (path-to-destination)

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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)

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)