Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments
Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments
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DOI：
10.1016/j.bpj.2013.12.055
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发表时间：
2014-03-18
影响因子：
3.4
通讯作者：
Gonzalez, Ruben L., Jr.
中科院分区：
文献类型：
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作者：
van de Meent, Jan-Willem;Bronson, Jonathan E.;Gonzalez, Ruben L., Jr.
许多单分子实验旨在根据动力学模型来描述生物分子过程，该模型指定生物分子构象状态之间的转换速率。估计这些速率通常需要对分子群进行分析，其中每个分子的构象轨迹由一个有噪声的、随时间变化的信号轨迹表示。虽然隐马尔可夫模型（hmm）可以用来推断单个分子的构象轨迹，但从推断的构象轨迹总体估计一致的动力学模型仍然是一项统计上困难的任务，因为推断的参数在一个群体中变化很大。在这里，我们展示了最近开发的hmm经验贝叶斯方法如何扩展，以使单分子荧光共振能量转移（smFRET）实验分析中的两个广泛发生的任务更加自动化和统计原则的方法：1)，在可变条件下进行的一系列实验中速率变化的表征；2)检测具有相同FRET效率但跃迁速率不同的简并态。我们将这种新开发的方法应用于细菌核糖体的两项研究，每项研究都是这两项分析任务中的一项。最后，我们讨论了模型选择技术，以确定适当数量的构象状态。用于执行此分析的代码和基本图形用户界面前端可以作为开源软件获得。
Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.