Open Access

ARTICLE

Urdnet: A Cryo-EM Particle Automatic Picking Method

by Jianquan Ouyang¹, Yue Zhang¹, Kun Fang^1,2,*, Tianming Liu³, Xiangyu Pan²

1 School of Computer Science & School of Cyberspace Science, Xiangtan University, Xiangtan, 411105, China
2 Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, 410118, China
3 Department of Computer Science, The University of Georgia, Athens, Georgia, USA

* Corresponding Author: Kun Fang. Email:

Computers, Materials & Continua 2022, 72(1), 1593-1610. https://doi.org/10.32604/cmc.2022.025072

Received 10 November 2021; Accepted 06 January 2022; Issue published 24 February 2022

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.

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Keywords

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