FEATHER: automated analysis of force spectroscopy unbinding and unfolding data via a Bayesian algorithm

Single-molecule force spectroscopy (SMFS) provides a powerful tool to explore the dynamics and energetics of individual proteins, protein-ligand interactions, and nucleic acid structures. In the canonical assay, a force probe is retracted at constant velocity to induce a mechanical unfolding/unbinding event. Next, two energy landscape parameters, the zero-force dissociation rate constant (ko) and the distance to the transition state (Δx‡), are deduced by analyzing the most probable rupture force as a function of the loading rate, the rate of change in force. Analyzing the shape of the rupture force distribution reveals additional biophysical information, such as the height of the energy barrier (ΔG‡). Accurately quantifying such distributions requires high-precision characterization of the unfolding events and significantly larger data sets. Yet, identifying events in SMFS data is often done in a manual or semiautomated manner and is obscured by the presence of noise. Here, we introduce...单分子力谱（Single-molecule force spectroscopy, SMFS）为探究单个蛋白质、蛋白质-配体相互作用及核酸结构的动力学与能量学特征提供了强有力的研究工具。在经典实验方案中，研究人员以恒定速度回缩力探针，以引发机械解折叠或解结合事件。随后，通过分析最概然破裂力随加载速率（即力的变化速率）的变化关系，可推导出两个能量面参数：零力解离速率常数（zero-force dissociation rate constant, kₒ）与至过渡态的距离（Δx‡）。对破裂力分布形态进行分析，还可获取额外的生物物理信息，例如能垒高度（ΔG‡）。若要对这类分布进行精准定量表征，需对解折叠事件开展高精度表征，同时依赖规模显著更大的数据集。然而，单分子力谱数据中的事件识别通常采用人工或半自动化方式，且常被数据中的噪声所掩盖。在此，我们引入……