DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM
Abstract Background Cryo-electron microscopy (Cryo-EM) is widely used in the
determination of the three-dimensional (3D) structures of macromolecules. Particle picking
from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the
diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs.
Because of these issues, significant human intervention is often required to generate a high-
quality set of particles for input to the downstream structure determination steps. Results …
determination of the three-dimensional (3D) structures of macromolecules. Particle picking
from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the
diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs.
Because of these issues, significant human intervention is often required to generate a high-
quality set of particles for input to the downstream structure determination steps. Results …
Background
Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps.
Results
Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention.
Conclusions
Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.
Springer
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