PT  - JOURNAL ARTICLE
AU  - Sanchez-Garcia, R
AU  - Gomez-Blanco, J
AU  - Cuervo, A
AU  - Carazo, JM
AU  - Sorzano, COS
AU  - Vargas, J
TI  - DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
AID  - 10.1101/2020.06.12.148296
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.12.148296
4099  - http://biorxiv.org/content/early/2020/08/17/2020.06.12.148296.short
4100  - http://biorxiv.org/content/early/2020/08/17/2020.06.12.148296.full
AB  - 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 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 obtain much cleaner and more detailed versions of the experimental maps. Additionally, we illustrated the benefits of DeepEMhancer on the structure of the SARS-CoV-2 RNA polymerase.Competing Interest StatementThe authors have declared no competing interest.