Abstract
BackgroundCryo-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.ResultsHere 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.ConclusionsOur 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.
Highlights
Cryo-electron microscopy (Cryo-EM) is widely used in the determina‐ tion of the three-dimensional (3D) structures of macromolecules
There are some challenges that those methods are facing such as lacking diversified training datasets, false-positive numerosity, and low-SNR micrographs accommodation
We evaluate the performance results of our DeepCryoPicker comparing with different particle picking tools such as RELION-2 [31], PIXER [4], DeepPicker [20], and DeepEM [6] based on the precision, recall, accuracy, and f1-score that are defined by Eqs. (1), (2), (3), and (4) respectively
Summary
Cryo-electron microscopy (Cryo-EM) is widely used in the determina‐ tion 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. Deep learning methods for single particle picking have been proposed, including EMAN2.21 [18], DeepEM [6], DeepPicker [20], and FasetParticlePicker [21].
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