ArticleNature communications2023
Improvement of cryo-EM maps by simultaneous local and non-local deep learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers.
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.
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.
Who cites it
158 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- Transcription remodeling and stalling by cisplatin-induced interstrand crosslinks.Nature chemical biology · 2026Article
- Receptor-induced activation of MERS-CoV spike revealed by time-course cryo-EM.PLoS pathogens · 2026Article
- Article
- Article
- Cryo-EM structures of multiple-peptide resistance factor (MprF) from Pseudomonas aeruginosa.The FEBS journal · 2026Article
- Structural Basis of Shiga Toxin Neutralization by Human Antibodies Targeting Canonical Receptor-Binding and Non-Canonical Assembly Sites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Structures of dynamic interactors at native proteasomes by PhIX-MS and cryo-electron microscopy.Molecular cell · 2026Article
- Interactive segmentation of membrane and membrane-mimic densities in cryo-EM maps.Acta crystallographica. Section D, Structural biology · 2026Article
- Nucleocapsid-like cryo-EM structure of influenza B virus.PLoS pathogens · 2026Article
- Molecular mechanism of pore formation by Plasmodium Perforin-like Protein 2.Nature communications · 2026Article
- FlyTomo: a streamlined software for on-the-fly cryo-ET data processing and diagnosis.Nature communications · 2026Article
- Eukaryotic-like microtubules and dynamic instability of Asgard archaeal tubulins.Science advances · 2026Article
- The bacterial mechanosensitive channel MscM gates through concerted changes in its transmembrane and cytoplasmic domains.Nature communications · 2026Article
- Confidence-guided cryo-EM map optimisation with LocScale-2.0.Nature communications · 2026Article
- TRiC-assisted folding of class I HDAC family proteins regulated by distinct co-chaperone and cofactor networks.Science advances · 2026Article
- Cryo-EM structures of human FANCJ reveal the mechanism of G-quadruplex unwinding and disease-associated mutations.Nature communications · 2026Article
- ADAPT-M: a workflow for rapid, quantitative in vitro measurements of enriched protein libraries.Nature communications · 2026Article
- Structure-guided reprogramming of DELLA turnover for a sustainable Green Revolution in rice.Nature communications · 2026Article
- L-type pyocins inhibit the BAM complex to kill without cell entry.Nature communications · 2026Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0-6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building.
Identifiers
What OpenQuestion holds
Registered trials
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.