Dataset Information

2.9 A Resolution Cryo-EM 3D Reconstruction of Close-Packed Virus Particles.

ABSTRACT: Single-particle cryoelectron microscopy typically discards close-packed particle images as unusable data. Here, we report an image processing strategy and case study of obtaining near-atomic resolution 3D reconstructions from close-packed particles. Multiple independent de novo initial models were constructed to determine and cross-validate the particle parameters. The particles with consistent views were further refined including not only Euler angles and center positions but also defocus, astigmatism, beam tilt, and overall and anisotropic magnification. We demonstrated this strategy with a 2.9 Å resolution reconstruction of a 1.67 MDa virus-like particle of a circovirus, PCV2, recorded on 86 photographic films. The map resolution was further validated with a phase-randomization test and local resolution assessment, and the atomic model was validated with MolProbity and EMRinger. Close-packed virus particles were thus shown not only to be useful for high-resolution 3D reconstructions but also to allow data collection at significantly improved throughput for near-atomic resolution reconstructions.

SUBMITTER: Liu Z

PROVIDER: S-EPMC4740195 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Publications

2.9 Å Resolution Cryo-EM 3D Reconstruction of Close-Packed Virus Particles.

Liu Zheng Z Guo Fei F Wang Feng F Li Tian-Cheng TC Jiang Wen W

Structure (London, England : 1993) 20160114 2

Single-particle cryoelectron microscopy typically discards close-packed particle images as unusable data. Here, we report an image processing strategy and case study of obtaining near-atomic resolution 3D reconstructions from close-packed particles. Multiple independent de novo initial models were constructed to determine and cross-validate the particle parameters. The particles with consistent views were further refined including not only Euler angles and center positions but also defocus, asti ...[more]

PMID: 26777413

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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

2.9 A Resolution Cryo-EM 3D Reconstruction of Close-Packed Virus Particles.

Publications

2.9 Å Resolution Cryo-EM 3D Reconstruction of Close-Packed Virus Particles.

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