2021
DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM
JIMÉNEZ-MORENO, Amaya; David STŘELÁK; Jiří FILIPOVIČ; José María CARAZO; Carlos Óscar S. SORZANO et. al.Basic information
Original name
DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM
Authors
JIMÉNEZ-MORENO, Amaya (724 Spain); David STŘELÁK (203 Czech Republic, belonging to the institution); Jiří FILIPOVIČ (203 Czech Republic, guarantor, belonging to the institution); José María CARAZO and Carlos Óscar S. SORZANO
Edition
Journal of Structural Biology, San Diego,USA, Academic Press, 2021, 1047-8477
Other information
Language
English
Type of outcome
Article in a journal
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
United States of America
Confidentiality degree
is not subject to a state or trade secret
References:
Impact factor
Impact factor: 3.234
RIV identification code
RIV/00216224:14610/21:00121351
Organization unit
Institute of Computer Science
UT WoS
000756475200010
EID Scopus
2-s2.0-85102652992
Keywords in English
3D alignment; 3D reconstruction; Cryo-EM; Deep learning; Machine learning
Tags
Tags
International impact, Reviewed
Changed: 12/4/2022 16:50, Mgr. Alena Mokrá
Abstract
In the original language
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.
Links
MUNI/A/1411/2019, interní kód MU |
| ||
MUNI/A/1549/2020, interní kód MU |
|