DOI: 10.5061/dryad.1615c2p
title: Data from: FEATHER: automated analysis of force spectroscopy unbinding and unfolding data via a Bayesian algorithm

description: 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.
description: dna-100nms-1dsDNA force-extension curves, 100nms, folder 1/8dna-100nms-2dsDNA force-extension curves, 100nms, folder 2/8dna-100nms-3dsDNA force-extension curves, 100nm/s, folder 3/8dna-100nms-4dsDNA force-extension curves, 100nm/s, folder 4/8dna-100nms-5dsDNA force-extension curves, 100nm/s, folder 5/8dna-100nms-6dsDNA force-extension curves, 100nm/s, folder 6/8dna-100nms-7dsDNA force-extension curves, 100nm/s, folder 7/8dna-100nms-8dsDNA force-extension curves, 100nm/s folder 8/8dna-500nms-1dsDNA force-extension curves, 500nms, folder 1/2dna-500nms-2dsDNA force-extension curves, 500nms, folder 2/2dna-1000nms-1dsDNA force-extension curves, 1000nm/s, folder 1/1polyprotein-various-nms-1polyprotein (Alpha3D+4xNUG2) force-extension curves, various speeds, folder 1/1Simulation-1Simulated data folder 1/2Simulation-2Simulated data folder 2/20_READMEREADME for all data here.