Evidence map›Paper›PMID 38862790›Full record

ArticleNature methods2024

Data-driven regularization lowers the size barrier of cryo-EM structure determination.

Dari Kimanius, Kiarash Jamali, Max E Wilkinson, Sofia Lövestam, Vaithish Velazhahan, Takanori Nakane, Sjors H W Scheres

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 156 papers.

0numbers the graph read from it
0cells of the map it votes in
156citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

156 citing papers in PubMed.

  1. Article
  2. Visualization of membrane-stabilized SorCS2Journal of structural biology: X · 2026
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  8. Diversity of electron-bifurcating COScience advances · 2026
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  14. LowDoseWizard: rapid and standardized setup of low-dose cryo-TEM imaging in SerialEM.Acta crystallographica. Section D, Structural biology · 2026
    Article
  15. Particles of echovirus 18 open to release their genomes in vivo.Proceedings of the National Academy of Sciences of the United States of America · 2026
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96 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Dari KimaniusMRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK. dari.kimanius@czii.org.
Kiarash JamaliMRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK.
Max E WilkinsonBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Sofia LövestamMRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK.
Vaithish VelazhahanMRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK.
Takanori NakaneInstitute for Protein Research, Osaka University, Suita-shi, Osaka, Japan.
Sjors H W ScheresMRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK. scheres@mrc-lmb.cam.ac.uk.ORCID http://orcid.org/0000-0002-0462-6540

Funding

RCUK | Medical Research Council (MRC) MC UP A025 1013
6 · The paper itself

Abstract

Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this approach, which we call Blush regularization, yields better reconstructions than do existing algorithms, in particular for data with low signal-to-noise ratios. The reconstruction of a protein-nucleic acid complex with a molecular weight of 40 kDa, which was previously intractable, illustrates that denoising neural networks will expand the applicability of cryo-EM structure determination for a wide range of biological macromolecules.

Indexed as

Cryoelectron MicroscopyImage Processing, Computer-AssistedAlgorithmsDeep LearningMacromolecular SubstancesModels, MolecularNeural Networks, ComputerSignal-To-Noise RatioMacromolecular Substances

Identifiers

PMID38862790
PMCPMC11239489

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.