Deconvolution of dynamic mechanical networks
Michael Hinczewski, Yann von Hansen, Roland R. Netz

TL;DR
This paper introduces a new dynamic deconvolution method to extract kinetic properties of components in complex mechanical networks from single-molecule experiments, enabling detailed analysis of conformational fluctuations and internal friction.
Contribution
We developed a dynamic deconvolution theory to determine intrinsic response functions of molecules within complex networks from equilibrium fluctuation data.
Findings
Successfully applied to simulated force spectroscopy data
Accurately extracted state-dependent protein diffusivity
Revealed physical insights into conformational fluctuations
Abstract
Time-resolved single-molecule biophysical experiments yield data that contain a wealth of dynamic information, in addition to the equilibrium distributions derived from histograms of the time series. In typical force spectroscopic setups the molecule is connected via linkers to a read-out device, forming a mechanically coupled dynamic network. Deconvolution of equilibrium distributions, filtering out the influence of the linkers, is a straightforward and common practice. We have developed an analogous dynamic deconvolution theory for the more challenging task of extracting kinetic properties of individual components in networks of arbitrary complexity and topology. Our method determines the intrinsic linear response functions of a given molecule in the network, describing the power spectrum of conformational fluctuations. The practicality of our approach is demonstrated for the…
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