Data from: 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, 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.