DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM

Abstract

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 (SNR) of micrographs. Because of these issues,

The transformational role of GPU computing and deep learning in drug discovery

“…High-throughput and automation of cryo-EM have become increasingly important as the state-of-the-art experimental technique used for protein structure determination for use in structure-based drug design 36 . DL-based approaches, such as DEFMap 37 and DeepPicker 38 , have been developed to accelerate processing of cryo-EM images. The DEFMap method directly extracts structure dynamics associated with hidden atomic fluctuations by combining DL and molecular dynamics simulations that learn the relationships between local density data.…”

“…DeepPicker employs convolutional neural networks (CNNs) and cross-molecule training to capture common features of particles from previously analysed micrographs, which facilities automatic particle picking in single-particle analysis. This tool serves to illustrate that DL integration can successfully address current gaps towards fully automated cryo-EM pipelines, paving the way for a new multidisciplinary approach to protein science 37,38 .…”

High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines

“…Many algorithms have also been developed to accelerate cryo-EM data preprocessing and minimize subjective decisions and tedious human annotations. Notably, deep learning, especially convolutional neural networks (CNNs), has greatly changed and improved the step of particle picking (Al-Azzawi et al, 2019;Bepler et al, 2019;Nguyen et al, 2019;Tegunov and Cramer, 2019;Wagner et al, 2019;Wang et al, 2016;Xiao and Yang, 2017;Zhang et al, 2019;Zhu et al, 2017). Nevertheless, the field still lacks a robust tool that will make decisions by evaluating the output from data preprocessing steps so that human intervention can be removed, making an automatically streamlining workflow possible.…”

High-throughput cryo-EM enabled by user-free preprocessing routines

“…Many algorithms have also been developed to accelerate cryo-EM data preprocessing and minimize subjective decisions and tedious human annotations. Notably, deep learning, especially convolutional neural network (CNN), has greatly changed and improved the step of particle picking (Wagner et al 2019;Bepler et al 2019;Tegunov and Cramer 2019;Wang et al 2016;Zhu, Ouyang, and Mao 2017;Zhang et al 2019;Xiao and Yang 2017;Nguyen et al 2019;Al-Azzawi et al 2019) . Nevertheless, the field still lacks a robust tool that will make decisions by evaluating the output from data preprocessing steps, so that human intervention can be removed, making an automatically streamlining workflow possible.…”