skip to main content
Idioma:

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

Sanchez-Garcia, Ruben ; Gomez-Blanco, Josue ; Cuervo, Ana ; Carazo, Jose Maria ; Sorzano, Carlos Oscar S. ; Vargas, Javier

Communications biology, 2021-07, Vol.4 (1), p.874-874, Article 874 [Periódico revisado por pares]

London: Nature Publishing Group

Texto completo disponível

Citações Citado por
  • Título:
    DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
  • Autor: Sanchez-Garcia, Ruben ; Gomez-Blanco, Josue ; Cuervo, Ana ; Carazo, Jose Maria ; Sorzano, Carlos Oscar S. ; Vargas, Javier
  • Assuntos: Biology ; Deep learning ; DNA-directed RNA polymerase ; Electron microscopy ; Noise reduction ; Protein structure ; RNA polymerase ; Severe acute respiratory syndrome coronavirus 2
  • É parte de: Communications biology, 2021-07, Vol.4 (1), p.874-874, Article 874
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 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.
  • Editor: London: Nature Publishing Group
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.