A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy

By Yanan Zhu, Ouyang Qi, Youdong Mao. 2016

140 citations recorded by the source corpus.

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly “knowledgeable”. Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

Subfields: Structural Biology, Surfaces, Coatings and Films

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Citations in the corpus

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing (2020, methodology): “Deep learning in cryo-EM was ﬁ rst applied to the problem of particle picking 9 – 11 and since then, it has evolved to deal with other questions such as map reconstruction 12,13 , map segmentation 14,15 , or local resolution determination 16,17 .”
SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM (2019, methodology): “To solve this problem, two particle selection programs, Dee-pEM by Wang et al. 8 and DeepPicker by Zhu et al. 9 , have been recently published, which employ deep convolutional neural networks (CNNs).”
Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression (2018, background): “If we implement low picking thresholds, it is likely that these programs pick not only particles, but also noise patterns known as false positives [7].”
Cryogenic Electron Microscopy for Characterizing and Diagnosing Batteries (2018, background): “We also expect the employment of advanced artificial intelligence to acquire and analyze the cryo-EM images automatically.(44,45)”