van de Meent Jan-Willem, Bronson Jonathan E, Wiggins Chris H, Gonzalez Ruben L
Department of Statistics, Columbia University, New York, New York.
Department of Chemistry, Columbia University, New York, New York.
Biophys J. 2014 Mar 18;106(6):1327-37. doi: 10.1016/j.bpj.2013.12.055.
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.
许多单分子实验旨在根据动力学模型来表征生物分子过程，这些模型规定了生物分子构象状态之间的转变速率。对这些速率的估计通常需要对一群分子进行分析，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。尽管隐马尔可夫模型（HMM）可用于推断单个分子的构象轨迹，但从推断出的构象轨迹群体中估计一个共识动力学模型仍然是一项统计上困难的任务，因为推断出的参数在群体中变化很大。在这里，我们展示了如何扩展一种最近开发的用于HMM的经验贝叶斯方法，以实现一种更自动化且符合统计原则的方法，用于单分子荧光共振能量转移（smFRET）实验分析中两个广泛存在的任务：1），表征在可变条件下进行的一系列实验中速率的变化；2），检测具有相同FRET效率但转变速率不同的简并状态。我们将这种新开发的方法应用于对细菌核糖体的两项研究，每项研究都是这两个分析任务之一的典型示例。我们最后讨论了用于确定合适构象状态数量的模型选择技术。用于执行此分析的代码和一个基本的图形用户界面前端作为开源软件提供。
