Abstract
BackgroundAn important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.ResultsWe design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles.ConclusionsAutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination.
Highlights
An important task of macromolecular structure determination by cryo-electron microscopy is the identification of single particles in micrographs
We evaluate the performance of AutoCryoPicker in the three stages according to multiple metrics such as clustering accuracy, particle misclassification rate, Dice, and time complexity
Accurate particle picking in cryo-electron microscopy (cryo-EM) images still requires substantial human intervention and, can be labor-intensive and time-consuming
Summary
An important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and are not fully automated. Single-particle cryo-electron microscopy (cryo-EM) was traditionally used to provide low resolution structural information on large protein complexes that resisted crystallization Numerous computational approaches have been proposed to facilitate the particle picking process [8,9,10,11,12,13,14] These methods can roughly be divided into two categories: generative methods [15,16,17] and discriminative classification methods [18,19,20] (e.g. the recent deep learning methods [21, 22]). The discriminative methods first train a classifier on a labeled dataset of positive and negative particle examples, apply it to detecting particle images from micrographs images
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