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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Main Authors: L. Fábregas Ibáñez, G. Jeschke, S. Stoll
Format: Article
Language:English
Published: Copernicus Publications 2020-10-01
Series:Magnetic Resonance
Online Access:https://mr.copernicus.org/articles/1/209/2020/mr-1-209-2020.pdf
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spelling 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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AT gjeschke deerlabacomprehensivesoftwarepackageforanalyzingdipolarelectronparamagneticresonancespectroscopydata
AT sstoll deerlabacomprehensivesoftwarepackageforanalyzingdipolarelectronparamagneticresonancespectroscopydata
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