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This study introduces a Bayesian approach for analyzing double electron-electron resonance (DEER) data. This method provides more robust protein conformational information by quantifying uncertainties in distance distributions.

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Area of Science:

  • Biophysics
  • Structural Biology
  • Computational Chemistry

Background:

  • Double electron-electron resonance (DEER) spectroscopy is crucial for determining spin-label distances in proteins, offering insights into conformational dynamics.
  • Analyzing DEER signals to obtain distance distributions is challenging due to the ill-posed nature of the inversion problem.

Purpose of the Study:

  • To develop a robust Bayesian probabilistic inference method for analyzing DEER data.
  • To accurately determine distance distributions and quantify uncertainties in protein structural analysis.

Main Methods:

  • Utilized a Bayesian probabilistic inference framework with a multi-Gauss mixture model for distance distributions.
  • Employed Markov chain Monte Carlo (MCMC) sampling to derive posterior probability distributions over model parameters.

Main Results:

  • The Bayesian approach successfully infers protein distance distributions from DEER data.
  • Quantified parameter uncertainties and captured distance distribution uncertainties via posterior predictive distributions.
  • Demonstrated the importance of model checking and comparison using residual analysis and Bayes factors on synthetic and experimental data.

Conclusions:

  • The proposed Bayesian method offers a more robust approach for inferring protein conformations from DEER spectroscopy.
  • This probabilistic framework provides a comprehensive quantification of uncertainty, enhancing structural and energetic insights.