Dataset Information

Precise beam-tilt alignment and collimation are required to minimize the phase error associated with coma in high-resolution cryo-EM.

ABSTRACT: Electron microscopy at a resolution of 0.4nm or better requires more careful adjustment of the illumination than is the case at a resolution of 0.8nm. The use of current-axis alignment is not always sufficient, for example, to avoid the introduction of large phase errors, at higher resolution, due to axial coma. In addition, one must also ensure that off-axis coma does not corrupt the data quality at the higher resolution. We particularly emphasize that the standard CTF correction does not account for the phase error associated with coma. We explain the cause of both axial coma and the typically most troublesome component of off-axis coma in terms of the well-known shift of the electron diffraction pattern relative to the optical axis that occurs when the illumination is not parallel to the axis. We review the experimental conditions under which coma causes unacceptably large phase errors, and we discuss steps that can be taken when setting up the conditions of illumination, so as to ensure that neither axial nor off-axis coma is a problem.

SUBMITTER: Glaeser RM

PROVIDER: S-EPMC3056894 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Precise beam-tilt alignment and collimation are required to minimize the phase error associated with coma in high-resolution cryo-EM.

Glaeser Robert M RM Typke Dieter D Tiemeijer Peter C PC Pulokas James J Cheng Anchi A

Journal of structural biology 20101221 1

Electron microscopy at a resolution of 0.4nm or better requires more careful adjustment of the illumination than is the case at a resolution of 0.8nm. The use of current-axis alignment is not always sufficient, for example, to avoid the introduction of large phase errors, at higher resolution, due to axial coma. In addition, one must also ensure that off-axis coma does not corrupt the data quality at the higher resolution. We particularly emphasize that the standard CTF correction does not accou ...[more]

PMID: 21182964

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Project description:BackgroundCryo-EM data generated by electron tomography (ET) contains images for individual protein particles in different orientations and tilted angles. Individual cryo-EM particles can be aligned to reconstruct a 3D density map of a protein structure. However, low contrast and high noise in particle images make it challenging to build 3D density maps at intermediate to high resolution (1-3 Å). To overcome this problem, we propose a fully automated cryo-EM 3D density map reconstruction approach based on deep learning particle picking.ResultsA perfect 2D particle mask is fully automatically generated for every single particle. Then, it uses a computer vision image alignment algorithm (image registration) to fully automatically align the particle masks. It calculates the difference of the particle image orientation angles to align the original particle image. Finally, it reconstructs a localized 3D density map between every two single-particle images that have the largest number of corresponding features. The localized 3D density maps are then averaged to reconstruct a final 3D density map. The constructed 3D density map results illustrate the potential to determine the structures of the molecules using a few samples of good particles. Also, using the localized particle samples (with no background) to generate the localized 3D density maps can improve the process of the resolution evaluation in experimental maps of cryo-EM. Tested on two widely used datasets, Auto3DCryoMap is able to reconstruct good 3D density maps using only a few thousand protein particle images, which is much smaller than hundreds of thousands of particles required by the existing methods.ConclusionsWe design a fully automated approach for cryo-EM 3D density maps reconstruction (Auto3DCryoMap). Instead of increasing the signal-to-noise ratio by using 2D class averaging, our approach uses 2D particle masks to produce locally aligned particle images. Auto3DCryoMap is able to accurately align structural particle shapes. Also, it is able to construct a decent 3D density map from only a few thousand aligned particle images while the existing tools require hundreds of thousands of particle images. Finally, by using the pre-processed particle images, Auto3DCryoMap reconstructs a better 3D density map than using the original particle images.

Dataset Information

Precise beam-tilt alignment and collimation are required to minimize the phase error associated with coma in high-resolution cryo-EM.

Publications

Precise beam-tilt alignment and collimation are required to minimize the phase error associated with coma in high-resolution cryo-EM.

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