Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules.

Researchers

Journal

Modalities

Models

Abstract

Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining three-dimensional structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules.
Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template/feature-matching and particle-classification methods. Applications to 6 large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM.
The PARSED package and user manual for noncommercial use are available as Supplementary Materials (in the compressed file: parsed_v1.zip).
Supplementary data are available at Bioinformatics online.
© The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].