scholarly journals High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

10.3791/55448 ◽  
2017 ◽  
Author(s):  
Toshio Moriya ◽  
Michael Saur ◽  
Markus Stabrin ◽  
Felipe Merino ◽  
Horatiu Voicu ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Resolution Single ◽  
Microscopy Images

A3Near-atomic Resolution Single Particle Analysis with the Volta Phase Plate

Microscopy ◽  
2015 ◽  
Vol 64 (suppl 1) ◽  
pp. i9.2-i9
Author(s):  
Radostin Danev ◽  
Maryam Khoshouei ◽  
Wolfgang Baumeister
Keyword(s):  
Single Particle ◽  
Atomic Resolution ◽  
Particle Analysis ◽  
Phase Plate ◽  
Single Particle Analysis ◽  
Resolution Single

scholarly journals A Self-Supervised Workflow for Particle Picking in Cryo-EM

2020 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

AbstractHigh-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, we have developed a self-supervised workflow. Our workflow includes an iterative strategy to use the 2D class average to improve training particles and a progressively improved convolutional neural network (CNN) for particle picking. To automate the selection of particles, we define a threshold (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. Our workflow has been tested using six publicly available data sets with different particle sizes and shapes, and is able to automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. Our workflow offers a way toward automated single-particle Cryo-EM data analysis at the stage of particle picking. The workflow may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE, and EMAN2.

scholarly journals Cryo-EM single particle analysis with the Volta phase plate

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Radostin Danev ◽  
Wolfgang Baumeister
Keyword(s):  
Single Particle ◽  
Spherical Aberration ◽  
Quality Data ◽  
Particle Analysis ◽  
Limiting Factor ◽  
Phase Plate ◽  
Single Particle Analysis ◽  
High Quality Data ◽  
Size Heterogeneity ◽  
Resolution Single

We present a method for in-focus data acquisition with a phase plate that enables near-atomic resolution single particle reconstructions. Accurate focusing is the determining factor for obtaining high quality data. A double-area focusing strategy was implemented in order to achieve the required precision. With this approach we obtained a 3.2 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome. The phase plate matches or slightly exceeds the performance of the conventional defocus approach. Spherical aberration becomes a limiting factor for achieving resolutions below 3 Å with in-focus phase plate images. The phase plate could enable single particle analysis of challenging samples in terms of small size, heterogeneity and flexibility that are difficult to solve by the conventional defocus approach.

scholarly journals A self-supervised workflow for particle picking in cryo-EM

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 719-727 ◽  
Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Single Particle ◽  
Particle Analysis ◽  
Data Sets ◽  
Particle Sizes ◽  
User Input ◽  
Class Average ◽  
Resolution Single ◽  
Selection Of

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE and EMAN2.

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