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Magnetic Resonance An interactive open-access publication of the Groupement AMPERE
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https://doi.org/10.5194/mr-2020-13
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/mr-2020-13
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 09 Jun 2020

Submitted as: research article | 09 Jun 2020

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This preprint is currently under review for the journal MR.

DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data

Luis Fábregas Ibáñez1, Gunnar Jeschke1, and Stefan Stoll2 Luis Fábregas Ibáñez et al.
  • 1ETH Zurich, Laboratory of Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
  • 2University of Washington, Department of Chemistry, Seattle, WA 98195, USA

Abstract. Dipolar 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 MATLAB toolbox 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, and run global analysis with parameter-free distributions. Important aspects of uncertainty analysis are discussed as well.

Luis Fábregas Ibáñez et al.

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Luis Fábregas Ibáñez et al.

Luis Fábregas Ibáñez et al.

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Latest update: 11 Jul 2020
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Short summary
Dipolar EPR spectroscopy methods such as DEER provide data on how proteins change shape, thus giving detailed insight into how proteins work. We present DeerLab, a comprehensive open-source software for completely and reliably analyzing the associated data. The software implements a series of theoretical and algorithmic innovations and thereby improves the quality and reproducibility of data analysis.
Dipolar EPR spectroscopy methods such as DEER provide data on how proteins change shape, thus...
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