Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models.

ABSTRACT: Double electron-electron resonance (DEER) spectroscopy measures distance distributions between spin labels in proteins, yielding important structural and energetic information about conformational landscapes. Analysis of an experimental DEER signal in terms of a distance distribution is a nontrivial task due to the ill-posed nature of the underlying mathematical inversion problem. This work introduces a Bayesian probabilistic inference approach to analyze DEER data, using a multi-Gauss mixture model for the distance distribution. The method uses Markov chain Monte Carlo (MCMC) sampling to determine a posterior probability distribution over model parameter space. This distribution contains all the information available from the data, including a full quantification of the uncertainty about

SUBMITTER: Sweger SR 

PROVIDER: S-EPMC7846514 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature