DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data
<p>Dipolar electron paramagnetic resonance (EPR) spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributi...
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doaj-350d82821d9745d4be859e988e60471d2021-08-02T19:56:28ZengCopernicus PublicationsMagnetic Resonance2699-00162020-10-01120922410.5194/mr-1-209-2020DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy dataL. Fábregas Ibáñez0G. Jeschke1S. Stoll2Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, SwitzerlandLaboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, SwitzerlandDepartment of Chemistry, University of Washington, Seattle, WA 98195, USA<p>Dipolar electron paramagnetic resonance (EPR) spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source software package for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, run global analysis with non-parametric distributions, and use a bootstrapping approach to fully quantify the uncertainty in the analysis.</p>https://mr.copernicus.org/articles/1/209/2020/mr-1-209-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
L. Fábregas Ibáñez G. Jeschke S. Stoll |
spellingShingle |
L. Fábregas Ibáñez G. Jeschke S. Stoll DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data Magnetic Resonance |
author_facet |
L. Fábregas Ibáñez G. Jeschke S. Stoll |
author_sort |
L. Fábregas Ibáñez |
title |
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
title_short |
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
title_full |
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
title_fullStr |
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
title_full_unstemmed |
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
title_sort |
deerlab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data |
publisher |
Copernicus Publications |
series |
Magnetic Resonance |
issn |
2699-0016 |
publishDate |
2020-10-01 |
description |
<p>Dipolar electron paramagnetic resonance (EPR) spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source software package for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, run global analysis with non-parametric distributions, and use a bootstrapping approach to fully quantify the uncertainty in the analysis.</p> |
url |
https://mr.copernicus.org/articles/1/209/2020/mr-1-209-2020.pdf |
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