Trends in Deep Learning Approaches for Protein Structure Classification in Single Particle Analysis

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  • Mamizu Nobuya
    九州工業大学大学院情報工学府 株式会社システムインフロンティア
  • Tanaka Kotaro
    九州工業大学大学院情報工学研究院
  • Yasunaga Takuo
    九州工業大学大学院情報工学研究院

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Other Title
  • 単粒子解析におけるタンパク質構造分類のための深層学習アプローチの動向
  • タンリュウシ カイセキ ニ オケル タンパクシツ コウゾウ ブンルイ ノ タメ ノ シンソウ ガクシュウ アプローチ ノ ドウコウ

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<p>Cryo-EM single particle analysis can solve multiple protein structures contained in a sample by classification. However, the information on the dynamics between the solved structures is lost and it can only be inferred. About this problem, cryoDRGN, a deep learning approach for solving the three-dimensional reconstruction and structural classification that was announced in 2020, breaks away from discrete data partition. The method is based on an auto-encoder, and realizes continuous structural classification by constructing a latent space that separates the information depending on the projection parameters from the input particle image. In this paper, we explain conventional classification method in single particle analysis and deep learning topics that are the background of cryoDRGN. Then, as a benchmark for structural classification, we try three-dimensional reconstruction on the actual data of GroEL/ES having 6 kinds of complexes.</p>

Journal

  • KENBIKYO

    KENBIKYO 55 (3), 104-108, 2020-12-30

    The Japanese Society of Microscopy

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