Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction.

ABSTRACT:

Background

Cryo-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.

Results

A 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

SUBMITTER: Al-Azzawi A 

PROVIDER: S-EPMC7768659 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature