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DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data.

Luis Fábregas Ibáñez1, Gunnar Jeschke1, Stefan Stoll2

  • 1ETH Zurich, Laboratory of Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland.

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|September 27, 2021
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Summary

DeerLab is a new open-source software package that simplifies the analysis of dipolar Electron Paramagnetic Resonance (EPR) data. It addresses challenges in structural characterization of biological systems by providing a unified, modular platform for distance measurements.

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Area of Science:

  • Biophysics
  • Structural Biology
  • Spectroscopy

Background:

  • Dipolar Electron Paramagnetic Resonance (EPR) spectroscopy, including Double Electron-Electron Resonance (DEER), is crucial for nanometer-scale distance measurements in macromolecular and biological systems.
  • Analyzing EPR data is challenging due to the ill-posed nature of inverse problems and fragmented, specialized analysis tools hindering reproducibility and development.
  • Current limitations in data analysis impede the full potential of EPR for structural characterization.

Purpose of the Study:

  • To introduce DeerLab, a novel, open-source software package designed to streamline and enhance the analysis of dipolar EPR data.
  • To provide a modular and versatile platform that integrates a wide range of analytical methods for EPR data.
  • To overcome the limitations of existing, scattered analysis tools and facilitate comparison, reproducibility, and method development.

Main Methods:

  • Development of DeerLab, an open-source, modular software package for dipolar EPR data analysis.
  • Implementation of various analysis techniques including separable non-linear least squares, multi-pulse DEER data fitting, and global analysis with non-parametric distributions.
  • Integration of a bootstrapping approach for comprehensive uncertainty quantification.

Main Results:

  • DeerLab successfully performs one-step analysis and fits complex dipolar multi-pathway models to experimental data.
  • The software enables global analysis with non-parametric distributions, offering flexibility in modeling.
  • A bootstrapping approach within DeerLab provides robust quantification of analysis uncertainties.

Conclusions:

  • DeerLab offers a unified, powerful, and user-friendly solution for analyzing dipolar EPR data, enhancing structural characterization of biological systems.
  • The modular design and comprehensive methods in DeerLab promote reproducibility, facilitate method development, and improve the reliability of distance measurements.
  • This software package represents a significant advancement for the EPR spectroscopy community, enabling more robust structural insights.