ディープラーニングによる単分子光画像の自動化ステキオメトリー分析
Jiachao Xu1,2, Gege Qin1,2, Fang Luo1,2
1Key Laboratory of Molecular Nanostructure and Nanotechnology, CAS Research/Education Center for Excellence in Molecular Sciences , Institute of Chemistry, Chinese Academy of Sciences , Beijing 100190 , China.
Journal of the American Chemical Society
|April 6, 2019
まとめ
この研究は,タンパク質の光白化の出来事を正確に数えるための新しいディープラーニング方法を導入します. この進歩は,生理学的条件下でタンパク質複合体ステキオメトリーの決定の効率と精度を向上させる.
科学分野:
- バイオ物理学
- 生物化学
- コンピュータ生物学
背景:
- タンパク質複合体ステキオメトリーは細胞の機能と調節に不可欠です.
- 単分子光成像は,タンパク質のステキオメトリを決定するための方法を提供します.
- 騒々しい単分子光データから正確な光白化イベントを抽出することは,光点滅のために困難です.
研究 の 目的:
- フォトブリーチイベントを正確に数えるための新しいディープラーニング方法を開発する.
- 単一分子光画像を用いたタンパク質ステキオメトリー測定の効率と精度を向上させる.
- フォトブリーチとフォトブレンチを区別する伝統的なアルゴリズムの限界を克服する.
主な方法:
- 収束型および長期短期記憶の深層学習ニューラルネットワーク (CLDNN) の開発.
- 光白化ドロップの特徴を抽出するためにコンボリュレーション層を使用します.
- 長期短期記憶 (LSTM) の再帰層を用いて,光白化と光点滅を区別する.
主要な成果:
- CLDNN方法は,光白化イベントのカウントにおいて,かなり高い精度を示しています.
- 従来のアルゴリズムと比較して 少なくとも2桁の効率向上を達成しました
- この方法は,ユーザー指定のパラメータを必要とせず,in vitroおよび細胞画像データ (EGFR) で検証されます.
結論:
- CLDNN方法は,タンパク質ステキオメトリー研究のための堅牢で効率的な戦略を提供します.
- このディープラーニングのアプローチは 化学と生物学における タイムシリーズ分析の新たな道を開きます
- この技術は,定量的生物学的分析のための単分子光成像の信頼性を高めます.
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