FEATHER: Automated Analysis of Force Spectroscopy Unbinding and Unfolding Data via~a~Bayesian Algorithm

Author
Abstract
<p>Single-molecule force\&nbsp;spectroscopy\&nbsp;(SMFS) provides a powerful tool to explore the dynamics and energetics of individual proteins,\&nbsp;protein-ligand interactions, and\&nbsp;nucleic acid structures. In the canonical assay, a force probe is retracted at constant velocity to induce a mechanical unfolding/unbinding event. Next, two\&nbsp;energy landscape\&nbsp;parameters, the zero-force dissociation rate constant (ko) and the distance to the transition state (Δx<sup>\textdaggerdbl</sup>), 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<sup>\textdaggerdbl</sup>). 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, to our knowledge, a new algorithm, FEATHER (force extension analysis using a testable hypothesis for event recognition), to automatically identify the locations of unfolding/unbinding events in SMFS records and thereby deduce the corresponding rupture force and loading rate. FEATHER requires no knowledge of the system under study, does not bias data interpretation toward the dominant behavior of the data, and has two easy-to-interpret, user-defined parameters. Moreover, it is a linear algorithm, so it scales well for large data sets. When analyzing a data set from a polyprotein containing both mechanically labile and robust domains, FEATHER featured a 30-fold improvement in event location precision, an eightfold improvement in a measure of the accuracy of the loading rate and rupture force distributions, and a threefold reduction of false positives in comparison to two representative reference algorithms. We anticipate FEATHER being leveraged in more complex analysis schemes, such as the segmentation of complex force-extension curves for fitting to worm-like chain models and extended in future work to data sets containing both unfolding and refolding transitions.</p>
Year of Publication
2018
Journal
Biophysical Journal
Volume
115
Number of Pages
757-762
Date Published
2018-09
ISSN Number
00063495
URL
https://reader.elsevier.com/reader/sd/pii/S0006349518309184?token=D16E717D5F15BDF89D89463DE1FC9FAB3A9C629A20FC17965BA3E0B40A58974E5E974EB61017A554F3440DE945D761F1
DOI
10.1016/j.bpj.2018.07.031
JILA PI
Journal Article