A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

ABSTRACT:

Background

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs.

Results

We developed a deep learning-based algorithmic

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC5521087 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature