DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.
Commun Biol
; 4(1): 874, 2021 07 15.
Article
in English
| MEDLINE | ID: covidwho-1387496
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Viral Proteins
/
DNA-Directed RNA Polymerases
/
Cryoelectron Microscopy
/
Deep Learning
/
SARS-CoV-2
Type of study:
Experimental Studies
Language:
English
Journal:
Commun Biol
Year:
2021
Document Type:
Article
Affiliation country:
S42003-021-02399-1
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