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Bayesian optimization to estimate hyperfine couplings from 19F ENDOR spectra.

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Summary

This study introduces Bayesian optimization to improve Electron Nuclear Double Resonance (ENDOR) spectral analysis for nuclear spin detection. This method enhances accuracy in determining distances within biomolecules using fluorine-19 (¹⁹F) labels.

Keywords:
Bayesian optimizationEPRElectron nuclear double resonanceFluorine labellingLeast-squares fittingSpectral simulation

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Area of Science:

  • Magnetic Resonance Spectroscopy
  • Biophysical Chemistry
  • Computational Chemistry

Background:

  • Electron Nuclear Double Resonance (ENDOR) spectroscopy is crucial for detecting nuclear spins near paramagnetic centers and their hyperfine interactions.
  • Fluorine-19 (¹⁹F) nuclear labels offer potential for ENDOR-based distance determination in biomolecules, complementing pulsed dipolar spectroscopy.
  • Spectral analysis in ENDOR is challenging due to large parameter spaces, broad resonances, and potential broadening from chemical shift anisotropy at high EPR frequencies.

Purpose of the Study:

  • To examine a statistical approach for optimizing parameter fitting in ¹⁹F ENDOR spectra.
  • To implement Bayesian optimization for rapid, global parameter searching in ENDOR spectral analysis.
  • To refine parameter fitting using gradient-based procedures after initial Bayesian optimization.

Main Methods:

  • Utilized two nitroxide-fluorine model systems for experimental 263 GHz ¹⁹F ENDOR spectra analysis.
  • Applied Bayesian optimization for efficient global parameter searching with minimal prior knowledge.
  • Employed a gradient-based fitting procedure for refining parameter estimates.
  • Developed an accelerated simulation procedure for analyzing two- and three-spin systems.

Main Results:

  • Demonstrated that Bayesian optimization followed by gradient-based fitting yields physically reasonable solutions for nitroxide-fluorine systems.
  • Showcased the ability to distinguish between minima of similar loss functions using Density Functional Theory (DFT) predictions.
  • The approach successfully provided stochastic error estimates for the determined parameters.

Conclusions:

  • The proposed statistical approach, combining Bayesian optimization and gradient-based fitting, significantly improves ¹⁹F ENDOR spectral analysis.
  • This method enhances the accuracy and reliability of distance determination in biomolecules using ¹⁹F labels.
  • Future work will focus on further developments and applications of this optimized ENDOR spectral analysis technique.