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- EMDB-19825: Subtomogram average of nucleosomes annotated by template matching... -

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Basic information

Entry
Database: EMDB / ID: EMD-19825
TitleSubtomogram average of nucleosomes annotated by template matching in isolated mitotic chromosomes
Map dataPrimary map
Sample
  • Complex: Nucleosome
KeywordsNucleosome / CELL CYCLE
Biological speciesGallus gallus (chicken)
Methodsubtomogram averaging / cryo EM / Resolution: 12.89 Å
AuthorsPatra G / Harastani M / Eltsov M
Funding support France, 2 items
OrganizationGrant numberCountry
Agence Nationale de la Recherche (ANR)ANR-20-CE11-0020-02 France
Agence Nationale de la Recherche (ANR)ANR-23-CE45-0012-01 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
DepositionMar 12, 2024-
Header (metadata) releaseMar 26, 2025-
Map releaseMar 26, 2025-
UpdateOct 15, 2025-
Current statusOct 15, 2025Processing site: PDBe / Status: Released

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Structure visualization

Supplemental images

emd_19825.png

Downloads & links

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Map

FileDownload / File: emd_19825.map.gz / Format: CCP4 / Size: 8 MB / Type: IMAGE STORED AS FLOATING POINT NUMBER (4 BYTES)
AnnotationPrimary map
Projections & slices

Image control

Size
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AxesZ (Sec.)Y (Row.)X (Col.)
4.15 Å/pix.
x 128 pix.
= 531.2 Å		4.15 Å/pix.
x 128 pix.
= 531.2 Å		4.15 Å/pix.
x 128 pix.
= 531.2 Å

Surface

Projections

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Images are generated by Spider.

Voxel sizeX=Y=Z: 4.15 Å
Density

Histogram

Histogram (log scale)
Contour LevelBy AUTHOR: 1.0
Minimum - Maximum-0.26171246 - 4.976783
Average (Standard dev.)0.0040822704 (±0.08945975)
SymmetrySpace group: 1
Details

EMDB XML:

Map geometry
Axis orderXYZ
Origin000
Dimensions128128128
Spacing128128128
CellA=B=C: 531.2 Å
α=β=γ: 90.0 °

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Supplemental data

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Mask #1

Fileemd_19825_msk_1.map
Projections & Slices
AxesZYX

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Half map: half map 2

Fileemd_19825_half_map_1.map
Annotationhalf map 2
Projections & Slices
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Histogram

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Half map: half map 1

Fileemd_19825_half_map_2.map
Annotationhalf map 1
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Sample components

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Entire : Nucleosome

EntireName: Nucleosome
Components
  • Complex: Nucleosome

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Supramolecule #1: Nucleosome

SupramoleculeName: Nucleosome / type: complex / ID: 1 / Parent: 0
Source (natural)Organism: Gallus gallus (chicken) / Tissue: DT-40 culture cells
Molecular weightTheoretical: 200 KDa

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Experimental details

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Structure determination

Methodcryo EM
Processingsubtomogram averaging
Aggregation stateparticle

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Sample preparation

BufferpH: 9
GridModel: Quantifoil / Material: COPPER / Mesh: 200 / Support film - Material: CARBON / Support film - topology: HOLEY
VitrificationCryogen name: ETHANE

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Electron microscopy

MicroscopeTFS KRIOS
Image recordingFilm or detector model: GATAN K3 (6k x 4k) / Detector mode: COUNTING / Average electron dose: 3.2 e/Å2
Electron beamAcceleration voltage: 300 kV / Electron source: FIELD EMISSION GUN
Electron opticsIllumination mode: OTHER / Imaging mode: BRIGHT FIELD / Cs: 2.7 mm / Nominal defocus max: 4.5 µm / Nominal defocus min: 2.5 µm
Sample stageCooling holder cryogen: NITROGEN
Experimental equipment
Model: Titan Krios / Image courtesy: FEI Company

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Image processing

Final reconstructionApplied symmetry - Point group: C1 (asymmetric) / Resolution.type: BY AUTHOR / Resolution: 12.89 Å / Resolution method: FSC 0.143 CUT-OFF / Software - Name: RELION (ver. 4.0) / Number subtomograms used: 10250
ExtractionNumber tomograms: 1 / Number images used: 18000 / Method: template matching / Details: PyTom
CTF correctionSoftware - Name: Gctf / Type: PHASE FLIPPING ONLY
Final 3D classificationNumber classes: 10 / Software - Name: RELION (ver. 4.0)
Details: 18000 of particles were originally picked. Classification and alignment of these particles into 10 classes resulted in only 2 nucleosome-like classes that sum to 10.25k picks (57% of the original picks).
Final angle assignmentType: MAXIMUM LIKELIHOOD / Software - Name: RELION (ver. 4.0)
FSC plot (resolution estimation)

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