PIXER: an automated particle-selection method based on segmentation using a deep neural network.

ABSTRACT:

Background

Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network.

Results

First, to accommodate low-SNR conditions, we convert micrographs into probability density ma

SUBMITTER: Zhang J 

PROVIDER: S-EPMC6339297 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature