New tools for automated cryo-EM single-particle analysis in RELION-4.0

被引:429
|
作者
Kimanius, Dari [1 ]
Dong, Liyi [1 ]
Sharov, Grigory [1 ]
Nakane, Takanori [1 ]
Scheres, Sjors H. W. [1 ]
机构
[1] MRC Lab Mol Biol, Francis Crick Ave, Cambridge CB2 0QH, England
基金
英国医学研究理事会;
关键词
ELECTRON-MICROSCOPY; IMAGE; SOFTWARE; VISUALIZATION; RESOLUTION;
D O I
10.1042/BCJ20210708
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.
引用
收藏
页码:4169 / 4185
页数:17
相关论文
共 50 条
  • [41] Pushing the resolution limit by correcting the Ewald sphere effect in single-particle Cryo-EM reconstructions
    Zhu, Dongjie
    Wang, Xiangxi
    Fang, Qianglin
    Van Etten, James L.
    Rossmann, Michael G.
    Rao, Zihe
    Zhang, Xinzheng
    NATURE COMMUNICATIONS, 2018, 9
  • [42] Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin
    Afanasyev, Pavel
    Seer-Linnemayr, Charlotte
    Ravelli, Raimond B. G.
    Matadeen, Rishi
    De Carlo, Sacha
    Alewijnse, Bart
    Portugal, Rodrigo V.
    Pannu, Navraj S.
    Schatz, Michael
    van Heel, Marin
    IUCRJ, 2017, 4 : 678 - 694
  • [43] How Good Can Single-Particle Cryo-EM Become? What Remains Before It Approaches Its Physical Limits?
    Glaeser, Robert M.
    ANNUAL REVIEW OF BIOPHYSICS, VOL 48, 2019, 48 : 45 - 61
  • [44] AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images
    Al-Azzawi, Adil
    Ouadou, Anes
    Tanner, John J.
    Cheng, Jianlin
    BMC BIOINFORMATICS, 2019, 20 (1)
  • [45] Single-particle cryo-EM reveals conformational variability of the oligomeric VCC β-barrel pore in a lipid bilayer
    Sengupta, Nayanika
    Mondal, Anish Kumar
    Mishra, Suman
    Chattopadhyay, Kausik
    Dutta, Somnath
    JOURNAL OF CELL BIOLOGY, 2021, 220 (12)
  • [46] Single-particle cryo-EM structures from iDPC-STEM at near-atomic resolution
    Lazic, Ivan
    Wirix, Maarten
    Leidl, Max Leo
    de Haas, Felix
    Mann, Daniel
    Beckers, Maximilian
    Pechnikova, Evgeniya, V
    Muller-Caspary, Knut
    Egoavil, Ricardo
    Bosch, Eric G. T.
    Sachse, Carsten
    NATURE METHODS, 2022, 19 (09) : 1126 - 1136
  • [47] DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM
    Al-Azzawi, Adil
    Ouadou, Anes
    Max, Highsmith
    Duan, Ye
    Tanner, John J.
    Cheng, Jianlin
    BMC BIOINFORMATICS, 2020, 21 (01)
  • [48] Denoising and covariance estimation of single particle cryo-EM images
    Bhamre, Tejal
    Zhang, Teng
    Singer, Amit
    JOURNAL OF STRUCTURAL BIOLOGY, 2016, 195 (01) : 72 - 81
  • [49] Sub-2 Angstrom resolution structure determination using single-particle cryo-EM at 200 keV
    Wu, Mengyu
    Lander, Gabriel C.
    Herzik, Mark A., Jr.
    JOURNAL OF STRUCTURAL BIOLOGY-X, 2020, 4
  • [50] Focused classifications and refinements in high-resolution single particle cryo-EM analysis
    Barchet, Charles
    Frechin, Leo
    Holvec, Samuel
    Hazemann, Isabelle
    von Loeffelholz, Ottilie
    Klaholz, Bruno P.
    JOURNAL OF STRUCTURAL BIOLOGY, 2023, 215 (04)