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- EMDB-51694: Subtomogram average of nucleosomes extracted using Template Learn... -

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Basic information

Entry
Database: EMDB / ID: EMD-51694
TitleSubtomogram average of nucleosomes extracted using Template Learning from cryo-tomograms of Drosophila melanogaster embryos
Map dataSubtomogram average of nucleosomes extracted using Template Learning from cryo-tomograms of Drosophila melanogaster embryos
Sample
  • Organelle or cellular component: Drosophila melanogaster embryos
KeywordsComplex / Nucleosome / NUCLEAR PROTEIN
Biological speciesDrosophila melanogaster (fruit fly)
Methodsubtomogram averaging / cryo EM / Resolution: 17.0 Å
AuthorsEltsov M / Harastani M
Funding support France, 5 items
OrganizationGrant numberCountry
Agence Nationale de la Recherche (ANR)ANR-10-INSB-005 France
Agence Nationale de la Recherche (ANR)ANR-20-CE11-0020-02 France
Agence Nationale de la Recherche (ANR)ANR-23-CE45-0012-01 France
Agence Nationale de la Recherche (ANR)ANR-17-EURE-0023 France
Agence Nationale de la Recherche (ANR)ANR-23-CE45-0012-03 France
CitationJournal: Nat Commun / Year: 2025
Title: Template Learning: Deep learning with domain randomization for particle picking in cryo-electron tomography.
Authors: Mohamad Harastani / Gurudatt Patra / Charles Kervrann / Mikhail Eltsov /
Abstract: Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle ...Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of biomolecules and cellular components in their near-native state. A key challenge in cryo-ET data analysis is particle picking, often performed by template matching, which relies on cross-correlating tomograms with known structural templates. Current deep learning-based methods improve accuracy but require labor-intensive annotated datasets for supervised training. Here, we present Template Learning, a technique that combines deep learning accuracy with the convenience of training on biomolecular templates via domain randomization. Template Learning automates synthetic dataset generation, modeling molecular crowding, structural variability, and data acquisition variation, thereby reducing or eliminating the need for annotated experimental data. We show that models trained using Template Learning, and optionally fine-tuned with experimental data, outperform those trained solely on annotations. Furthermore, Template Learning provides higher precision and more uniform orientation detection than template matching, particularly for small non-spherical particles. Template Learning software is open-source, Python-based, and GPU/CPU parallelized.
History
DepositionOct 2, 2024-
Header (metadata) releaseOct 15, 2025-
Map releaseOct 15, 2025-
UpdateOct 15, 2025-
Current statusOct 15, 2025Processing site: PDBe / Status: Released

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Structure visualization

Supplemental images

emd_51694.png

Downloads & links

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Map

FileDownload / File: emd_51694.map.gz / Format: CCP4 / Size: 1 MB / Type: IMAGE STORED AS FLOATING POINT NUMBER (4 BYTES)
AnnotationSubtomogram average of nucleosomes extracted using Template Learning from cryo-tomograms of Drosophila melanogaster embryos
Projections & slices

Image control

Size
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AxesZ (Sec.)Y (Row.)X (Col.)
4.4 Å/pix.
x 64 pix.
= 281.6 Å		4.4 Å/pix.
x 64 pix.
= 281.6 Å		4.4 Å/pix.
x 64 pix.
= 281.6 Å

Surface

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Images are generated by Spider.

Voxel sizeX=Y=Z: 4.4 Å
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Histogram

Histogram (log scale)
Contour LevelBy AUTHOR: 0.006
Minimum - Maximum-0.028245796 - 0.04318713
Average (Standard dev.)-0.0000006987881 (±0.0034508044)
SymmetrySpace group: 1
Details

EMDB XML:

Map geometry
Axis orderXYZ
Origin000
Dimensions646464
Spacing646464
CellA=B=C: 281.6 Å
α=β=γ: 90.0 °

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Supplemental data

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Half map: half 2

Fileemd_51694_half_map_1.map
Annotationhalf 2
Projections & Slices
AxesZYX

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Half map: half 1

Fileemd_51694_half_map_2.map
Annotationhalf 1
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Histogram (log scale)

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Sample components

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Entire : Drosophila melanogaster embryos

EntireName: Drosophila melanogaster embryos
Components
  • Organelle or cellular component: Drosophila melanogaster embryos

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Supramolecule #1: Drosophila melanogaster embryos

SupramoleculeName: Drosophila melanogaster embryos / type: organelle_or_cellular_component / ID: 1 / Parent: 0
Details: Drosophila melanogaster embryos were high-pressure frozen. Vitreous sections were cut with a nominal thickness of 75 nm and collected onto 200 mesh C-flat grids
Source (natural)Organism: Drosophila melanogaster (fruit fly) / Strain: Bloomington Stock number 30564 / Organ: embryonic brain / Organelle: nucleus

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Experimental details

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Structure determination

Methodcryo EM
Processingsubtomogram averaging
Aggregation statecell

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Sample preparation

BufferpH: 7.5
GridModel: C-flat-2/2 / Material: COPPER / Mesh: 200
VitrificationCryogen name: NITROGEN / Details: high-pressure freezing HPM010.

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Electron microscopy

MicroscopeTFS KRIOS
Image recordingFilm or detector model: GATAN K2 SUMMIT (4k x 4k) / Detector mode: COUNTING / Average electron dose: 1.5 e/Å2
Electron beamAcceleration voltage: 300 kV / Electron source: FIELD EMISSION GUN
Electron opticsIllumination mode: OTHER / Imaging mode: BRIGHT FIELD / Nominal defocus max: 3.5 µm / Nominal defocus min: 2.75 µm
Experimental equipment
Model: Titan Krios / Image courtesy: FEI Company

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Image processing

Final reconstructionApplied symmetry - Point group: C1 (asymmetric) / Algorithm: BACK PROJECTION / Resolution.type: BY AUTHOR / Resolution: 17.0 Å / Resolution method: FSC 0.143 CUT-OFF / Number subtomograms used: 2000
ExtractionNumber tomograms: 1 / Number images used: 2000
CTF correctionType: PHASE FLIPPING AND AMPLITUDE CORRECTION
Final angle assignmentType: OTHER
Details: Exhaustive search based on constrained cross correlation

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