Probabilistic inference of distance distributions in DEER spectroscopy

Abstract

Double electron-electron (DEER) spectroscopy measures distance distributions between pairs of spin labels site-selectively attached to proteins, thereby revealing structural and energetic information about protein conformational landscapes. We present a Bayesian inference approach for analyzing DEER data, based on a model that assumes a nonparametric distance distribution P with a Tikhonov-like smoothness prior. It uses Markov chain Monte Carlo (MCMC) sampling that combines Gibbs and Hamilton Monte Carlo (HMC) sampling to generate samples of the posterior distribution of P and all other model parameters, given an experimental dataset.