Saurabh Ayush, Wisna Gde Bimananda Mahardika, Schweiger Maxwell C, Hariadi Rizal F, Presse Steve
bioRxiv. 2025 Aug 27:2025.06.12.659382. doi: 10.1101/2025.06.12.659382.
Förster resonance energy transfer (FRET) is a widely used tool to probe nanometer scale dynamics, projecting rich 3D biomolecular motion onto noisy 1D traces. However, interpretation of FRET traces remains challenging due to degeneracy-distinct structural states map to similar FRET efficiencies-and often suffers from under- and/or over-fitting due to the need to predefine the number of FRET states and noise characteristics. Here we provide a new software, Bayesian nonparametric FRET (BNP-FRET) for binned data obtained from integrative detectors, that eliminates user-dependent parameters and accurately incorporates all known noise sources, enabling the identification of distinct configurations from 1D traces in a plug-n-play manner. Using simulated and experimental data, we demonstrate that BNP-FRET eliminates logistical barrier of predetermining states for each FRET trace and permits high-throughput, simultaneous analysis of a large number of kinetically heterogeneous traces. Furthermore, working in the Bayesian paradigm, BNP-FRET naturally provides uncertainty estimates for all model parameters including the number of states, kinetic rates, and FRET efficiencies.
Förster共振能量转移（FRET）是一种广泛用于探测纳米尺度动力学的工具，它将丰富的三维生物分子运动投影到有噪声的一维轨迹上。然而，由于简并性——不同的结构状态对应相似的FRET效率，FRET轨迹的解释仍然具有挑战性，并且由于需要预先定义FRET状态的数量和噪声特征，常常存在欠拟合和/或过拟合的问题。在这里，我们提供了一种新的软件，用于处理从集成探测器获得的分箱数据的贝叶斯非参数FRET（BNP-FRET），该软件消除了用户依赖的参数，并准确纳入了所有已知的噪声源，能够以即插即用的方式从一维轨迹中识别出不同的构型。使用模拟和实验数据，我们证明BNP-FRET消除了为每个FRET轨迹预先确定状态的逻辑障碍，并允许对大量动力学异质轨迹进行高通量、同时分析。此外，在贝叶斯范式下工作，BNP-FRET自然地为所有模型参数提供不确定性估计，包括状态数量、动力学速率和FRET效率。
