  Background: Single-particle cryo-electron microscopy (cryo-EM) has become a
popular tool for structural determination of biological macromolecular
complexes. 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 of particle
picking often use low-resolution templates as inputs for particle matching,
making it possible to cause reference-dependent bias. It is critical to develop
a highly efficient template-free method to automatically recognize 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) of eight layers, which can be recursively
trained to be highly "knowledgeable". Our approach exhibits improved
performance and high precision when tested on the standard KLH dataset.
Application of DeepEM to several challenging experimental cryo-EM datasets
demonstrates its capability in avoiding selection of un-wanted particles and
non-particles even when true particles contain fewer features. Conclusions: The
DeepEM method derived from a deep CNN allows automated particle extraction from
raw cryo-EM micrographs in the absence of templates, which demonstrated
improved performance, objectivity and accuracy. Application of this novel
approach is expected to free the labor involved in single-particle
verification, thus promoting the efficiency of cryo-EM data processing.
