DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.

ABSTRACT:

Background

Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.

Results

Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automat

SUBMITTER: Al-Azzawi A 

PROVIDER: S-EPMC7653784 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature