Direct determination of kinetic rates from single-molecule photon arrival trajectories using hidden Markov models
Direct determination of kinetic rates from single-molecule photon arrival trajectories using hidden Markov models
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DOI：
10.1021/jp035514
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发表时间：
2003-09-25
影响因子：
2.9
通讯作者：
Talaga, DS
中科院分区：
文献类型：
--
作者：
Andrec, M;Levy, RM;Talaga, DS
从单个蛋白质分子的荧光测量已成为一个重要的新工具，在动态过程的研究，允许由单个蛋白质和大分子复合物所经历的运动的直接可视化。来自这种单分子实验的数据是光子轨迹的形式，包括单个光子的到达时间和波长信息。对光子轨迹的分析可能是困难的，特别是如果运动以与光子到达速率相当的速率发生或者在存在噪声的情况下。在本文中，我们介绍了使用隐马尔可夫模型（HALGOR）的光子轨迹数据的分析，直接使用光子数据，而不需要合奏平均的数据所暗示的相关函数分析。使用一个简单的动力学模型，我们研究的运动速率和光子探测率的估计的不确定性之间的关系。值得注意的是，我们得到的速率常数的相对不确定性低至3%，即使当相互转换率等于光子探测率，和不确定性增加到只有10%时，相互转换率是光子探测率的10倍。这表明，有用的信息可以获得更快的动力学制度比通常研究。我们还研究了背景光子对确定速率的影响，并证明了基于HMM的方法是鲁棒的，背景光子到达速率接近信号的不确定性很小。这些结果不仅是相关的，在建立理论上的精度限制，但在实验设计的背景下也是有用的。最后，为了证明该方法如何可以扩展到更复杂的动力学模型，以及它如何可以允许人们利用统计的全部力量进行模型评估和选择，我们考虑了Schenter等人先前研究的蛋白质构象转变的四态动力学模型（J. Phys. Chem. A 1999，103，10477）。我们展示了如何HMM可以被用来作为一种替代高阶相关函数分析检测的“构象记忆”和明显的非马尔可夫动力学所产生的这种时间不均匀的动力学计划。
The measurement of fluorescence from single protein molecules has become an important new tool in the study of dynamic processes, allowing for the direct visualization of the motions experienced by individual proteins and macromolecular complexes. The data from such single-molecule experiments are in the form of photon trajectories, consisting of arrival times and wavelength information on individual photons. The analysis of photon trajectories can be difficult, particularly if the motions are occurring at rates comparable to the photon arrival rate or in the presence of noise. In this paper, we introduce the use of hidden Markov models (HMMs) for the analysis of photon trajectory data that operate using the photon data directly, without the need for ensemble averaging of the data as implied by correlation function analysis. Using a simple kinetic model, we examine the relationship between the uncertainty in the estimates of the motional rate and the photon detection rate. Remarkably, we obtain relative uncertainties in the rate constants of as little as 3% even when the interconversion rate is equal to the photon detection rate, and the uncertainty increases to only 10% when the interconversion rate is 10 times the photon detection rate. This suggests that useful information can be obtained for much faster kinetic regimes than have typically been studied. We also examine the impact of background photons on the determination of the rate and demonstrate that the HMM-based approach is robust, displaying small uncertainties for background photon arrival rates approaching that of the signal. These results not only are relevant in establishing the theoretical limits on precision, but are also useful in the context of experimental design. Finally, to demonstrate how the methodology can be extended to more complex kinetic models and how it can allow one to make use of the full power of statistics for purposes of model evaluation and selection, we consider a four-state kinetic model for protein conformational transitions previously studied by Schenter et al. (J. Phys. Chem. A 1999, 103, 10477). We show how an HMM can be used as an alternative to higher-order correlation function analysis for the detection of "conformational memory" and apparent non-Markovian dynamics arising from such temporally inhomogeneous kinetic schemes.