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Rubén Sánchez-García, Josué Gómez-Blanco, Ana Cuervo, J.M. Carazo, Carlos Óscar S. Sorzano, Javier Vargas
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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Sánchez-García, R., Gómez-Blanco, J., Cuervo, A., Carazo, J., Sorzano, C. Ó. S., & Vargas, J. (2021). DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Communications Biology, 4(1), 874. https://doi.org/10.1038/s42003-021-02399-1
Sánchez-García, Rubén, et al. "DeepEMhancer: a deep learning solution for cryo-EM volume post-processing." Communications Biology, vol. 4, no. 1, 2021, pp. 874, https://doi.org/10.1038/s42003-021-02399-1.
Sánchez-García, Rubén, Josué Gómez-Blanco, Ana Cuervo, J.M. Carazo, Carlos Óscar S. Sorzano, and Javier Vargas. "DeepEMhancer: a deep learning solution for cryo-EM volume post-processing." Communications Biology 4, no. 1 (2021): 874. https://doi.org/10.1038/s42003-021-02399-1.
Sánchez-García R., Gómez-Blanco J., Cuervo A. [и др.] DeepEMhancer: a deep learning solution for cryo-EM volume post-processing // Communications Biology. — 2021. — Vol. 4, № 1. — P. 874. — URL: https://doi.org/10.1038/s42003-021-02399-1
@article{sanchezgarcia2021deepemhancer,
title = {{DeepEMhancer: a deep learning solution for cryo-EM volume post-processing}},
author = {Rubén Sánchez-García and Josué Gómez-Blanco and Ana Cuervo and J.M. Carazo and Carlos Óscar S. Sorzano and Javier Vargas},
journal = {Communications Biology},
year = {2021},
volume = {4},
number = {1},
pages = {874},
doi = {10.1038/s42003-021-02399-1},
url = {https://doi.org/10.1038/s42003-021-02399-1}
}TY - JOUR AU - Sánchez-García, Rubén AU - Gómez-Blanco, Josué AU - Cuervo, Ana AU - Carazo, J.M. AU - Sorzano, Carlos Óscar S. AU - Vargas, Javier TI - DeepEMhancer: a deep learning solution for cryo-EM volume post-processing JO - Communications Biology VL - 4 IS - 1 SP - 874 PY - 2021 DA - 2021/07/15 DO - 10.1038/s42003-021-02399-1 UR - https://doi.org/10.1038/s42003-021-02399-1 ER -
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