EMPIAR-13271 - SARS-COV-2-6P-MUT7 S PROTEIN [multiple data sets in TIFF format]

EMPIAR-13271

SARS-COV-2-6P-MUT7 S PROTEIN [multiple data sets in TIFF format]

Publication:

Optimization of VE607 to generate analogs with improved neutralization activities against SARS-CoV-2 variants

Ding S, Yang D, Ullah I, Niu L, Unger M, Díaz-Salinas MA, Chandravanshi M, Zhou F, Beaudoin-Bussières G, Benlarbi M, Tolbert WD, Yoon K-W, Xu R, Laroche G, Gaudette F, Morton AJ, Lang ZC, Son A, Abrams C, Côté M, Smith AB, Huang RK, Matthies D, Munro JB, Pazgier M, Uchil PD, Finzi A

Journal of virology 99 (2025)

PMID: 41081594

DOI: 10.1128/jvi.01034-25

Related EMDB entry:

EMD-70454 EMD-70453

Deposited:

2025-12-15

Released:

2026-02-09

Last modified:

2026-02-09

Imageset size:

3.84 TB

Imageset DOI:

10.6019/EMPIAR-13271

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. SARS-COV-2-6P-MUT7 S PROTEIN-1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

3000

Image size:

(None, None)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.416, 0.416)

Details:

0.416A/super-res pix.
0.832A/pix
0.05s per frame.
2.5sec exposure for total 50 frames.
54.2e/A2 total.
1.08e/A2 per frame
gain file gain_20240930.mrc
gain file is flipped in Y
files are compress with bzip2

Files:

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2. SARS-COV-2-6P-MUT7 S PROTEIN-2

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

2999

Image size:

(None, None)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.416, 0.416)

Details:

Files:

This entry cannot be downloaded from PDBj.

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

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3. SARS-COV-2-6P-MUT7 S PROTEIN-3

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

2809

Image size:

(None, None)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.416, 0.416)

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

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)

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)

Ultrathin liquid cells for microsecond time-resolved cryo-EM.

Curtis WA, Wenz J, Krüger CR, Barrass SV, Drabbels M, Lorenz UJ. (2026)

GTpick: A deep neural network for Cryo-EM particle detection.

Ni S, Yang C, Liu Y, Zhang Y, Shi Y, Qian A, Kong R, Chang S. (2025)

Species-specific structural adaptation of the potyviral coat protein in virions and virus-like particles.

Koritnik N, Kežar A, Kavčič L, Žnidarič MT, Leonardi A, De S, Pollari M, Mäkinen K, Podobnik M. (2026)

High-resolution ab initio reconstruction enables cryo-EM structure determination of small particles

Kim K, Li H, Clarke OB. (2025)

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.

Krentzel D, Elphick M, Domart MC, Peddie CJ, Laine RF, Shand C, Henriques R, Collinson LM, Jones ML. (2025)

CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.

Arora S, Pan SH, Kumar S, Singh SK, Chauhan U, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Chui KT, Gupta BB. (2025)

See all citations

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