ArticleCommunications biology2021
DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,028 papers.
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Who cites it
1,028 citing papers in PubMed.
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- Structural basis of an endoplasmic reticulum EMC:Spf1 insertase-dislocase complex.Nature structural & molecular biology · 2026Article
- Structural basis of diverse substrate recruitment by the HIV-1 Vpr-hijacked CRL4(VprBP) E3 ligase.PLoS pathogens · 2026Article
- Structural interplay of the redox co-chaperone CnoX to GroEL/ES chaperonin.Life science alliance · 2026Article
- Structural and genetic dissection of RNA-guided gene repression by TnpB-derived transcription factors.bioRxiv : the preprint server for biology · 2026Article
- Beyond Canonical CO Oxidation: Structural and Evolutionary Insights Into a Non-Canonical Carbon Monoxide Dehydrogenase.Angewandte Chemie (International ed. in English) · 2026Article
- Structural framework for the assembly of the human tRNA ligase complex.Nature communications · 2026Article
- Structural basis of diverse antibody recognition of conserved coronavirus spike S2 epitopes that contribute to protective immunity.bioRxiv : the preprint server for biology · 2026Article
968 more citing papers are in PubMed but not listed here.
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6 authors.
Funding
Abstract
Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on global B-factor correction, suffer from limitations. For instance, they ignore the heterogeneity in the map local quality that reconstructions tend to exhibit. Aiming to overcome these problems, we present DeepEMhancer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental maps and maps sharpened using their respective atomic models, DeepEMhancer has learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhancer was evaluated on a testing set of 20 different experimental maps, showing its ability to reduce noise levels and obtain more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.
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