Current Status and Prospects of Deep-learning-based AI Applications to Cryogenic Electron Microscopy Single Particle Analysis

Abstract

Single particle analysis using cryogenic electron microscopy has been rapidly developing as a high-resolution structure determination method for biological macromolecules. In particular, it has enabled direct visualization of macromolecules, to which other structure determination methods could not be applied due to size, structural heterogeneity and compositional variability of the structures. Since single particle analysis is an image-processing-heavy method, a wide variety of algorithms have been tried and many procedures have already been automated, and so the procedure is steadily becoming more routine work. However, until now, only general abilities of human visual recognition and comprehensive judgment, which are based on past experiences of many structural analysis practices, have been unable to be automated. However, deep learning techniques, which have revolutionized the AI field in recent years, are changing this situation. This article will give an overview of deep learning applications in various processing steps of single particle analysis, and introduce crYOLO and Topaz in more details as two best representatives of deep-learning-based particle picking that have reached a practical level of full automation.