CBS, Univ. Montpellier, CNRS, INSERM, Montpellier, France.
Phys Chem Chem Phys. 2018 Jul 11;20(27):18775-18781. doi: 10.1039/c8cp03056a.
Molecular motors convert chemical or electrical energy into mechanical displacement, either linear or rotary. Under ideal circumstances, single-molecule measurements can spatially and temporally resolve individual steps of the motor, revealing important properties of the underlying mechanochemical process. Unfortunately, steps are often hard to resolve, as they are masked by thermal noise. In such cases, details of the mechanochemistry can nonetheless be recovered by analyzing the fluctuations in the recorded traces. Here, we expand upon existing statistical analysis methods, providing two new avenues to extract the motor step size, the effective number of rate-limiting chemical states per translocation step, and the compliance of the link between the motor and the probe particle. We first demonstrate the power and limitations of these methods using simulated molecular motor trajectories, and we then apply these methods to experimental data of kinesin, the bacterial flagellar motor, and F1-ATPase.
分子马达将化学或电能转化为线性或旋转的机械位移。在理想情况下,单分子测量可以在空间和时间上分辨出马达的单个步骤,揭示出潜在机械化学过程的重要性质。不幸的是,由于热噪声的干扰,这些步骤往往难以分辨。在这种情况下,通过分析记录轨迹的波动,仍然可以恢复机械化学的细节。在这里,我们扩展了现有的统计分析方法,提供了两种新的途径来提取马达的步长、每个转位步骤中有效的限速化学状态数量以及马达和探针粒子之间的连接顺应性。我们首先使用模拟分子马达轨迹演示了这些方法的优势和局限性,然后将这些方法应用于驱动蛋白、细菌鞭毛马达和 F1-ATP 酶的实验数据。