EMPIAR-12098 - Particle stack from SARS-CoV-2 (wuhan variant) Spike protein in complex with the single chain fragment scFv41N single particle cryo-EM data [stack of 4991 particles in MRCS format]

EMPIAR-12098

Particle stack from SARS-CoV-2 (wuhan variant) Spike protein in complex with the single chain fragment scFv41N single particle cryo-EM data [stack of 4991 particles in MRCS format]

Publication:

SARS-CoV-2 (wuhan variant) Spike protein in complex with the single chain fragment scFv41N

Chaves-Sanjuan A, Berlinguer M, Bolognesi M

(---)

Related EMDB entry:

EMD-50420 EMD-50422 EMD-50421 EMD-50419

Deposited:

2024-05-26

Released:

2024-07-16

Last modified:

2024-07-16

Imageset size:

712.42 GB

Imageset DOI:

10.6019/EMPIAR-12098

Experimental metadata:

Download xml json

Contains:

picked particles - multiframe - unprocessed

Image Sets

1. Particle stack and .star files of the initial and the final set of particles of the SARS-CoV-2 (wuhan variant) Spike protein in complex with the single chain fragment scFv41N data set

Category:

picked particles - multiframe - unprocessed

Image format:

MRCS

No. of images or tilt series:

4991

Image size:

(400, 400)

Pixel type:

UNSIGNED 32 BIT INTEGER

Pixel spacing:

(0.889, 0.889)

Details:

The particle stack in the particle folder contains both particles of the initial set of picked particles and the final reconstruction.

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)

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)