EMPIAR-13707 - Bovine lactoferrin single particles in water, PDB-templated for uniform orientation. [4828 multi-frame micrographs composed of 64 frames each in TIFF format]

EMPIAR-13707

Bovine lactoferrin single particles in water, PDB-templated for uniform orientation. [4828 multi-frame micrographs composed of 64 frames each in TIFF format]

Publication:

Generation of Conformational Energy Landscape from Lactoferrin cryo-EM images with Mitigated Preferred Orientations by cryoTWIN

Wada M, Yamazaki K, Wada Y, Ohga K, Ishii Y, Nakagawa A, Yoshikawa H, Okuno S, Nojima T, Ochi H, Hirose M, Kato T, Katoh T

SPIE 14267 (2026)

DOI: 10.1117/12.3119418

Related EMDB entry:

EMD-68587

Deposited:

2025-12-22

Released:

2026-08-07

Last modified:

2026-08-07

Imageset size:

1.53 TB

Imageset DOI:

10.6019/EMPIAR-13707

Experimental metadata:

Download xml json

Contains:

micrographs - multiframe

Image Sets

1. imageset_1

Category:

micrographs - multiframe

Image format:

TIFF

No. of images or tilt series:

4828

Image size:

(4092, 5760)

Pixel type:

UNSIGNED BYTE

Pixel spacing:

(0.675, 0.675)

Details:

- Microscope: FEI TITAN Krios
- Detector: Gatan K3 BIOQUANTUM
- Acceleration voltage (kV): 300
- Spherical aberration (Cs) : 2.7
- Nominal Defocus (μm): 0.8 - 2.0
- Average electron dose per image (e-/A2): 50.2
- Please use 'CountCDSRef_240930_2_31-1_14.02.07_X-1Y-1-0.mrc' as a gain reference without rotation and y-flipping.
or
- Please use "CountCDSRef_240930_2_31-1_14.02.07_X-1Y-1-0.mrc" as a gain reference without rotation or flipping in cryoSPARC.

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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.

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

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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