Abstract
Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and pixel-level tags, used to accurately and automatically locate particles from cryo-electron microscopy images, and solve the bottleneck that cryo-EM Single-particle biological macromolecule reconstruction requires tens of thousands of automatically picked particles. The 80S ribosome, HCN1 channel and TcdA1 toxin subunits, and other public protein datasets have been trained and tested on Urdnet. The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION, DeepPicker, and acquire the 3D structure of picked particles with higher resolution.
Highlights
Cryo-Electron Microscopy (Cryo-EM) has become an essential structural biology technology
To further evaluate the quality of the particles selected by U-Net based residual intensive neural network (Urdnet), we compared our method to the semi-automatic selection method of RELION via the entire public datasets. 80S ribosomes (EMPIAR10153) contain 318 micrographs, HCN1 channel (EMPIAR-10081) is the largest dataset, including 997 micrographs, TcdA1 toxin subunit (EMPIAR-10089) contains 97 micrographs, and KLH consists of 82 micrographs from the US National Resource for Automated Molecular Microscopy (NRAMM). 2D classification in the RELION software worked on the picked particles to identify suitable 2D average classes
This paper proposes a residual dense convolutional network model based on multiple annotations and improved U-Net for automatic particle picking of cryo-EM biomacromolecules
Summary
Cryo-EM has become an essential structural biology technology. It freezes the sample and keeps it in the microscope at a low temperature. The highly coherent electrons, used as a light source, illuminate from above and are scattered by the sample and the nearby ice layer. The scatter signal is imaged and recorded using a detector and a lens system. Signal processing is performed to obtain the structure of the sample, which is a valuable means of understanding the mechanism of biochemical reactions. Just as the catalytic sites of some proteins are known through
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