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

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  • Moriya Toshio
    高エネルギー加速器研究機構　物質構造科学研究所　構造生物学研究センター

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  • クライオ電子顕微鏡単粒子解析への深層学習AI技術の応用の現状と展望
  • クライオ デンシ ケンビキョウタンリュウシ カイセキ エ ノ シンソウ ガクシュウ AI ギジュツ ノ オウヨウ ノ ゲンジョウ ト テンボウ

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Abstract

<p>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.</p>

Journal

  • KENBIKYO

    KENBIKYO 55 (3), 114-119, 2020-12-30

    The Japanese Society of Microscopy

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