DeepEM

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 framework, DeepEM, for single-particle recognition from noisy cryo-EM
micrographs, enabling automated particle picking, selection and verification in
an integrated fashion. The kernel of DeepEM is built upon a convolutional neural
network (CNN) composed of eight layers, which can be recursively trained to be
highly "knowledgeable". Our approach exhibits an improved performance and
accuracy when tested on the standard KLH dataset. Application of DeepEM to
several challenging experimental cryo-EM datasets demonstrated its ability to
avoid the selection of un-wanted particles and non-particles even when true
particles contain fewer features. CONCLUSIONS: The DeepEM methodology, derived
from a deep CNN, allows automated particle extraction from raw cryo-EM
micrographs in the absence of a template. It demonstrates an improved
performance, objectivity and accuracy. Application of this novel method is
expected to free the labor involved in single-particle verification,
significantly improving the efficiency of cryo-EM data processing.

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

DeepEM is a convolutional neural network (CNN) to detect single particles in noisy cryo-EM micrographs.

DeepEM

Topic

Detail

Publications

Download and documentation