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DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F21%3A00121351" target="_blank" >RIV/00216224:14610/21:00121351 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://www.sciencedirect.com/science/article/pii/S1047847721000174" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1047847721000174</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.jsb.2021.107712" target="_blank" >10.1016/j.jsb.2021.107712</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM

  • Popis výsledku v původním jazyce

    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.

  • Název v anglickém jazyce

    DeepAlign, a 3D Alignment Method based on Regionalized Deep Learning for Cryo-EM

  • Popis výsledku anglicky

    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.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2021

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název periodika

    Journal of Structural Biology

  • ISSN

    1047-8477

  • e-ISSN

    1095-8657

  • Svazek periodika

    213

  • Číslo periodika v rámci svazku

    2

  • Stát vydavatele periodika

    US - Spojené státy americké

  • Počet stran výsledku

    14

  • Strana od-do

    „107712“

  • Kód UT WoS článku

    000756475200010

  • EID výsledku v databázi Scopus

    2-s2.0-85102652992