单分子力谱中键势和键迁移率的贝叶斯场论重建
Bayesian field theoretic reconstruction of bond potential and bond mobility in single molecule force spectroscopy
摘要：
量化大分子之间和内部的力是理解分子结构、蛋白质折叠和酶功能和性能的力学的必要的第一步。在这样的大分子环境中，动态单分子力谱（DFS）已被用于扭曲键。由此产生的响应，在形式的断裂力，所施加的工作，和轨迹的位移，已被用来重建债券的潜力。这些方法通常依赖于一维键势的简单参数化，平衡起始状态的假设，和/或大量的轨迹数据。参数方法通常无法推断具有多个最小值的复杂形状的键势，而分段估计可能无法保证在大距离处具有适当行为的平滑结果。现有的技术，特别是那些基于功定理，也没有解决空间变化的扩散率，可能会出现从空间不均匀耦合到其他自由度的大分子，从而提出了一个不完整的图片的整体键动力学。为了解决这些挑战，我们开发了一种全面的经验贝叶斯方法，将数据和正则化项直接纳入路径积分。在我们的方法中，所有的实验和统计参数都是直接从数据中经验估计的。在模拟数据上测试我们的方法后，我们的正则化方法需要更少的数据，并允许同时推断复杂的键电位和扩散率曲线。
英文摘要：
Quantifying the forces between and within macromolecules is a necessary first step in understanding the mechanics of molecular structure, protein folding, and enzyme function and performance. In such macromolecular settings, dynamic single-molecule force spectroscopy (DFS) has been used to distort bonds. The resulting responses, in the form of rupture forces, work applied, and trajectories of displacements, have been used to reconstruct bond potentials. Such approaches often rely on simple parameterizations of one-dimensional bond potentials, assumptions on equilibrium starting states, and/or large amounts of trajectory data. Parametric approaches typically fail at inferring complex-shaped bond potentials with multiple minima, while piecewise estimation may not guarantee smooth results with the appropriate behavior at large distances. Existing techniques, particularly those based on work theorems, also do not address spatial variations in the diffusivity that may arise from spatially inhomogeneous coupling to other degrees of freedom in the macromolecule, thereby presenting an incomplete picture of the overall bond dynamics. To solve these challenges, we have developed a comprehensive empirical Bayesian approach that incorporates data and regularization terms directly into a path integral. All experiemental and statistical parameters in our method are estimated empirically directly from the data. Upon testing our method on simulated data, our regularized approach requires fewer data and allows simultaneous inference of both complex bond potentials and diffusivity profiles.
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