通过深度学习对单分子光成像痕迹的自动化立体测量分析
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
概括
这项研究引入了一种新的深度学习方法,用于准确计算蛋白质光漂白事件. 这一进步提高了在生理条件下确定蛋白质复合体的效率和精度.
科学领域:
- 生物物理
- 生物化学
- 计算生物学
背景情况:
- 蛋白质复合体静态测量对于细胞功能和调节至关重要.
- 单分子光成像提供了一种确定蛋白质静态度的方法.
- 从杂的单分子光数据中提取准确的光漂白事件是具有挑战性的.
研究的目的:
- 开发一种新的深度学习方法,用于准确的光漂白事件计数.
- 使用单分子光成像来提高蛋白质固体测量的效率和准确性.
- 克服传统算法的局限性,以区分光漂白和光闪.
主要方法:
- 开发一个卷积和长期短期记忆深度学习神经网络 (CLDNN).
- 使用卷积层提取光漂白滴的特征.
- 采用长期短期记忆 (LSTM) 的反复层来区分光漂白和光闪事件.
主要成果:
- 在光漂白事件计数中,CLDNN方法的准确性显著提高.
- 与传统算法相比,效率至少提高了2个数量级.
- 该方法不需要用户指定的参数,并通过体外和细胞成像数据 (EGFR) 进行验证.
结论:
- 通过CLDNN方法,为蛋白质固体测量研究提供了强大而有效的策略.
- 这种深度学习方法为化学和生物学中的时间序列分析提供了新的途径.
- 该技术提高了单分子光成像用于定量生物分析的可靠性.
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