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@article{1757923,
author = {JiménezandMoreno, Amaya and Střelák, David and Filipovič, Jiří and Carazo, José María and Sorzano, Carlos Óscar S.},
article_location = {San Diego,USA},
article_number = {2},
doi = {http://dx.doi.org/10.1016/j.jsb.2021.107712},
keywords = {3D alignment; 3D reconstruction; Cryo-EM; Deep learning; Machine learning},
language = {eng},
issn = {1047-8477},
journal = {Journal of Structural Biology},
title = {DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM},
url = {https://www.sciencedirect.com/science/article/pii/S1047847721000174},
volume = {213},
year = {2021}
}
TY - JOUR
ID - 1757923
AU - Jiménez-Moreno, Amaya - Střelák, David - Filipovič, Jiří - Carazo, José María - Sorzano, Carlos Óscar S.
PY - 2021
TI - DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM
JF - Journal of Structural Biology
VL - 213
IS - 2
SP - "107712"
EP - "107712"
PB - Academic Press
SN - 10478477
KW - 3D alignment
KW - 3D reconstruction
KW - Cryo-EM
KW - Deep learning
KW - Machine learning
UR - https://www.sciencedirect.com/science/article/pii/S1047847721000174
N2 - Cryo Electron Microscopy (Cryo-EM) is currently one of the main tools to reveal the structural information of biological specimens at high resolution. Despite the great development of the techniques involved to solve the biological structures with Cryo-EM in the last years, the reconstructed 3D maps can present lower resolution due to errors committed while processing the information acquired by the microscope. One of the main problems comes from the 3D alignment step, which is an error-prone part of the reconstruction workflow due to the very low signal-to-noise ratio (SNR) common in Cryo-EM imaging. In fact, as we will show in this work, it is not unusual to find a disagreement in the alignment parameters in approximately 20–40% of the processed images, when outputs of different alignment algorithms are compared. In this work, we present a novel method to align sets of single particle images in the 3D space, called DeepAlign. Our proposal is based on deep learning networks that have been successfully used in plenty of problems in image classification. Specifically, we propose to design several deep neural networks on a regionalized basis to classify the particle images in sub-regions and, then, make a refinement of the 3D alignment parameters only inside that sub-region. We show that this method results in accurately aligned images, improving the Fourier shell correlation (FSC) resolution obtained with other state-of-the-art methods while decreasing computational time.
ER -
JIMÉNEZ-MORENO, Amaya, David STŘELÁK, Jiří FILIPOVIČ, José María CARAZO a Carlos Óscar S. SORZANO. DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM. \textit{Journal of Structural Biology}. San Diego,USA: Academic Press, 2021, roč.~213, č.~2, s.~''107712'', 14 s. ISSN~1047-8477. Dostupné z: https://dx.doi.org/10.1016/j.jsb.2021.107712.
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