Advanced Science Institute, RIKEN, Wako, Saitama, Japan.
Biophys J. 2012 Sep 19;103(6):1315-24. doi: 10.1016/j.bpj.2012.07.047.
Single-molecule fluorescence resonance energy transfer (smFRET) measurement is a powerful technique for investigating dynamics of biomolecules, for which various efforts have been made to overcome significant stochastic noise. Time stamp (TS) measurement has been employed experimentally to enrich information within the signals, while data analyses such as the hidden Markov model (HMM) have been successfully applied to recover the trajectories of molecular state transitions from time-binned photon counting signals or images. In this article, we introduce the HMM for TS-FRET signals, employing the variational Bayes (VB) inference to solve the model, and demonstrate the application of VB-HMM-TS-FRET to simulated TS-FRET data. The same analysis using VB-HMM is conducted for other models and the previously reported change point detection scheme. The performance is compared to other analysis methods or data types and we show that our VB-HMM-TS-FRET analysis can achieve the best performance and results in the highest time resolution. Finally, an smFRET experiment was conducted to observe spontaneous branch migration of Holliday-junction DNA. VB-HMM-TS-FRET was successfully applied to reconstruct the state transition trajectory with the number of states consistent with the nucleotide sequence. The results suggest that a single migration process frequently involves rearrangement of multiple basepairs.
单分子荧光共振能量转移（smFRET）测量是一种研究生物分子动力学的强大技术，为此已经做出了各种努力来克服显著的随机噪声。时间戳（TS）测量已被实验用于丰富信号中的信息，而数据分析，如隐马尔可夫模型（HMM），已成功应用于从时间分箱光子计数信号或图像中恢复分子状态转变的轨迹。在本文中，我们引入了用于 TS-FRET 信号的 HMM，采用变分贝叶斯（VB）推断来解决模型，并展示了 VB-HMM-TS-FRET 在模拟 TS-FRET 数据中的应用。使用 VB-HMM 对其他模型和先前报道的检测点变化方案进行了相同的分析。将性能与其他分析方法或数据类型进行了比较，结果表明，我们的 VB-HMM-TS-FRET 分析可以实现最佳性能和最高时间分辨率的结果。最后，进行了 smFRET 实验来观察 Holliday 结 DNA 的自发分支迁移。VB-HMM-TS-FRET 成功地应用于重构与核苷酸序列一致的状态转变轨迹。结果表明，单个迁移过程经常涉及多个碱基对的重排。
