BACKGROUND:Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network. RESULTS:First, to accommodate low-SNR conditions, we convert micrographs into probability density maps using a segmentation network. These probability density maps indicate the likelihood that each pixel of a micrograph is part of a particle instead of just background noise. Particles selected from density maps have a more robust signal than do those directly selected from the original noisy micrographs. Second, at present, there is no segmentation-training dataset for cryo-EM. To enable our plan, we present an automated method to generate a training dataset for segmentation using real-world data. Third, we propose a grid-based, local-maximum method to locate the particles from the probability density maps. We tested our method on simulated and real-world experimental datasets and compared PIXER with the mainstream methods RELION, DeepEM and DeepPicker to demonstrate its performance. The results indicate that, as a fully automated method, PIXER can acquire results as good as the semi-automated methods RELION and DeepEM. CONCLUSION:To our knowledge, our work is the first to address the particle-selection problem using the segmentation network concept. As a fully automated particle-selection method, PIXER can free researchers from laborious particle-selection work. Based on the results of experiments, PIXER can acquire accurate results under low-SNR conditions within minutes.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Acknowledgments: The authors thank the National Supercomputer Center in Guangzhou (NSCC-GZ, China) for providing the Tianhe-2 supercomputer to support some of the intensive computations. Funding: This research was supported by the National Key Research and Development Program of China (2017YFE0103900 and 2017YFA0504702), NSFC grant nos. U1611263, U1611261, 61472397, 61502455, and 61672493 and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (second phase). The funding body did not play any role in the study design and collection, analysis, and interpretation of data and in writing the manuscript.