Bayesian Probabilistic Inference of Nonparametric Distance Distributions in DEER Spectroscopy | JoVE Visualize

Area of Science:

Biophysics
Structural Biology
Computational Chemistry

Background:

Double electron-electron resonance (DEER) spectroscopy is crucial for measuring distances between spin labels in proteins, providing insights into protein dynamics and conformational changes.
Analyzing DEER data to obtain distance distributions is challenging due to the ill-posed nature of the mathematical inversion required.
Existing methods like bootstrapping can be computationally intensive and may underestimate uncertainty.

Purpose of the Study:

To introduce a novel Bayesian probabilistic inference approach for analyzing DEER spectroscopic data.
To develop a method that accurately determines distance distributions and quantifies associated uncertainties.
To provide a faster and more robust alternative to existing DEER data analysis techniques.

Main Methods:

A Bayesian probabilistic inference framework was employed, assuming a nonparametric distance distribution with a Tikhonov smoothness prior.
Markov Chain Monte Carlo (MCMC) sampling, specifically a compositional Gibbs sampler, was utilized to explore the posterior probability distribution.
The method determines the full posterior distribution over model parameters, including the distance distribution, given experimental DEER data.

Main Results:

The Bayesian approach successfully analyzes DEER data, yielding posterior probability distributions for distance distributions.
Uncertainty in the distance distribution is visually represented through an ensemble of posterior predictive distributions.
The method demonstrated faster performance and provided slightly larger, more comprehensive uncertainty intervals compared to bootstrapping.

Conclusions:

The developed Bayesian inference method provides a powerful and efficient tool for analyzing DEER data.
This approach offers a robust quantification of uncertainty in protein distance distributions derived from DEER experiments.
The method enhances the structural and energetic insights obtainable from DEER spectroscopy, advancing the study of protein conformational landscapes.