Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Abstract

Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination. However, identifying a sufficient number of particles for analysis (often >100,000) can take months of manual effort. Current computational approaches are limited by high false positive rates and require significant ad-hoc post-processing, especially for unusually shaped particles. To address this shortcoming, we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with few labeled particles by newly leveraging the remaining unlabeled particles through the framework of positive-unlabeled (PU) learning. Remarkably, despite using minimal labeled particles, Topaz allows us to improve reconstruction resolution by up to 0.15 Å over published particles on three public cryoEM datasets without any post-processing. Furthermore, we show that our novel generalized-expectation criteria approach to PU learning outperforms existing general PU learning approaches when applied to particle detection, especially for challenging datasets of non-globular proteins. We expect Topaz to be an essential component of cryoEM analysis.

43 pages, 5 main figures, 6 supplemental figures

References in corpus (4)

• The VIA Annotation Software for Images, Audio and Video
• Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms
• A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
• DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM

Cited by in corpus (6)

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• Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM
• Unsupervised particle sorting for high-resolution single-particle cryo-EM
• Tensorial template matching for fast cross-correlation with rotations and its application for tomography
• Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis
• CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field