用隐马尔可夫模型检测FoF1-ATP合成酶旋转马达中的子步
Detecting substeps in the rotary motors of FoF1-ATP synthase by Hidden Markov Models
摘要：
FoF 1-ATP合成酶是为活细胞提供“化学能量货币”三磷酸腺苷（ATP）的酶。ATP的形成是通过酶内亚基的逐步内部旋转来完成的。我们监测亚基旋转的单分子荧光共振能量转移（FRET）的方法，使用两个荧光团特异性地连接到酶。为了识别ATP合成酶马达的旋转运动的步长，我们模拟了自由扩散酶的共聚焦单分子FRET数据，并开发了一种基于“隐马尔可夫模型”（HMM）的寻步算法。隐马尔可夫模型是能够找到的邻近因子，P，为一个三电平系统和一个五电平系统，并解开模拟旋转运动的停留时间。为了识别系统中隐藏状态的数量，针对应用于每组模拟数据的一个状态到八个状态的一系列的HISTORY计算似然参数。因此，实验的单分子FRET数据的基本先决条件被定义，允许之间的120度步进模式或36度的子步旋转模式的质子驱动的Fo马达的ATP合酶的歧视。
英文摘要：
FoF1-ATP synthase is the enzyme that provides the 'chemical energy currency' adenosine triphosphate, ATP, for living cells. The formation of ATP is accomplished by a stepwise internal rotation of subunits within the enzyme. We monitor subunit rotation by a single-molecule fluorescence resonance energy transfer (FRET) approach using two fluorophores specifically attached to the enzyme. To identify the stepsize of rotary movements by the motors of ATP synthase we simulated the confocal single-molecule FRET data of freely diffusing enzymes and developed a step finder algorithm based on 'Hidden Markov Models' (HMM). The HMM is able to find the proximity factors, P, for a three-level system and for a five-level system, and to unravel the dwell times of the simulated rotary movements. To identify the number of hidden states in the system, a likelihood parameter is calculated for the series of one-state to eight-state HMMs applied to each set of simulated data. Thereby, the basic prerequisites for the experimental single-molecule FRET data are defined that allow for discrimination between a 120 degree stepping mode or a 36 degree substep rotation mode for the proton-driven Fo motor of ATP synthase.
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