Center for Biological Physics, Department of Physics , Arizona State University , Tempe , Arizona 85287 , United States.
School of Computing, Informatics, and Decision Systems Engineering , Arizona State University , Tempe , Arizona 85287 , United States.
J Phys Chem B. 2019 Jan 24;123(3):675-688. doi: 10.1021/acs.jpcb.8b09752. Epub 2019 Jan 10.
We develop a Bayesian nonparametric framework to analyze single molecule FRET (smFRET) data. This framework, a variation on infinite hidden Markov models, goes beyond traditional hidden Markov analysis, which already treats photon shot noise, in three critical ways: (1) it learns the number of molecular states present in a smFRET time trace (a hallmark of nonparametric approaches), (2) it accounts, simultaneously and self-consistently, for photophysical features of donor and acceptor fluorophores (blinking kinetics, spectral cross-talk, detector quantum efficiency), and (3) it treats background photons. Point 2 is essential in reducing the tendency of nonparametric approaches to overinterpret noisy single molecule time traces and so to estimate states and transition kinetics robust to photophysical artifacts. As a result, with the proposed framework, we obtain accurate estimates of single molecule properties even when the supplied traces are excessively noisy, subject to photoartifacts, and of short duration. We validate our method using synthetic data sets and demonstrate its applicability to real data sets from single molecule experiments on Holliday junctions labeled with conventional fluorescent dyes.
我们开发了一种贝叶斯非参数框架来分析单分子 FRET（smFRET）数据。这种框架是无限隐马尔可夫模型的一种变体，它在三个关键方面超越了传统的隐马尔可夫分析：（1）它可以学习 smFRET 时间轨迹中存在的分子状态数量（这是非参数方法的标志）；（2）它可以同时和自洽地考虑供体和受体荧光团的光物理特性（闪烁动力学、光谱串扰、探测器量子效率）；（3）它可以处理背景光子。第 2 点对于减少非参数方法过度解释噪声单分子时间轨迹的趋势至关重要，从而可以稳健地估计状态和过渡动力学，避免光物理伪影的影响。因此，使用所提出的框架，即使提供的轨迹噪声过大、受到光伪影影响且持续时间较短，我们也可以获得单分子特性的准确估计。我们使用合成数据集验证了我们的方法，并展示了其在使用传统荧光染料标记的 Holliday 连接点上进行单分子实验的真实数据集上的适用性。
