Dataset Information

Single particle reconstruction and validation using Bsoft for the map challenge.

ABSTRACT: The Bsoft package is aimed at processing electron micrographs for the determination of the three-dimensional structures of biological specimens. Recent advances in hardware allow us to solve structures to near atomic resolution using single particle analysis (SPA). The Map Challenge offered me an opportunity to test the ability of Bsoft to produce reconstructions from cryo-electron micrographs at the best resolution. I also wanted to understand what needed to be done to work towards full automation with validation. Here, I present two cases for the Map Challenge using Bsoft: ß-galactosidase and GroEL. I processed two independent subsets in each case with resolution-limited alignment. In both cases the reconstructions approached the expected resolution within a few iterations of alignment. I further validated the results by coherency-testing: i.e., that the reconstructions from real particles give better resolutions than reconstructions from the same number of aligned noise images. The key operations requiring attention for full automation are: particle picking, faster accurate alignment, proper mask generation, appropriate map sharpening, and understanding the amount of data needed to reach a desired resolution.

SUBMITTER: Heymann JB

PROVIDER: S-EPMC6119472 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Single particle reconstruction and validation using Bsoft for the map challenge.

Heymann J Bernard JB

Journal of structural biology 20180705 1

The Bsoft package is aimed at processing electron micrographs for the determination of the three-dimensional structures of biological specimens. Recent advances in hardware allow us to solve structures to near atomic resolution using single particle analysis (SPA). The Map Challenge offered me an opportunity to test the ability of Bsoft to produce reconstructions from cryo-electron micrographs at the best resolution. I also wanted to understand what needed to be done to work towards full automat ...[more]

PMID: 29981840

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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

Single particle reconstruction and validation using Bsoft for the map challenge.

Publications

Single particle reconstruction and validation using Bsoft for the map challenge.

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