State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, 100084, Beijing, China.
Department of Mathematics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Commun Biol. 2024 Oct 28;7(1):1404. doi: 10.1038/s42003-024-07122-4.
Long-term single-molecule fluorescence measurements are widely used powerful tools to study the conformational dynamics of biomolecules in real time to further elucidate their conformational dynamics. Typically, thousands or even more single-molecule traces are analyzed to provide statistically meaningful information, which is labor-intensive and can introduce user bias. Recently, several deep-learning models have been developed to automatically classify single-molecule traces. In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. DEBRIS efficiently and accurately identifies both one-color and two-color single-molecule events, including their start and end points. By adjusting user-defined criteria, DEBRIS becomes the pioneer in using a deep learning model to accurately classify four different types of single-molecule fluorescence events using the same trained model, demonstrating its universality and ability to enrich the current toolbox.
长期的单分子荧光测量是一种广泛使用的强大工具，可用于实时研究生物分子的构象动力学，以进一步阐明它们的构象动力学。通常，需要分析数千个甚至更多的单分子轨迹，以提供具有统计学意义的信息，这是一项劳动密集型的工作，并且可能会引入用户偏差。最近，已经开发了几种深度学习模型来自动分类单分子轨迹。在这项研究中，我们引入了 DEBRIS（基于深度学习的片段化方法，用于单分子荧光事件识别），这是一种深度学习模型，专注于分类局部特征，能够自动识别稳定的荧光信号和不同模式的动态出现的信号。DEBRIS 可以高效准确地识别单分子的单、双色事件，包括它们的起点和终点。通过调整用户定义的标准，DEBRIS 成为先驱，使用相同的训练模型准确地分类四种不同类型的单分子荧光事件，展示了其普遍性和丰富当前工具集的能力。
