Confidence Analysis of DEER Data and its Structural Interpretation with Ensemble-Biased Metadynamics

Abstract

Given its ability to measure multicomponent distance distributions between electron-spin probes, Double Electron-Electron Resonance spectroscopy (DEER) has become a leading technique to assess the structural dynamics of biomolecules. Recently, we have published two new methods for advancing the rigorous interpretation of DEER data (https://doi.org/10.1016/j.bpj.2018.08.008). First, building upon a model-based approach in which the distance probability distribution is represented as a sum of Gaussians or other functions, we use propagation of errors to calculate an associated confidence band for the distance distribution. This approach considers all sources of uncertainty, including the experimental noise, the uncertainty in the fitted background signal, and the limited time-span of the data collection. The resulting confidence band reveals the most and least reliable features of the probability distribution, thereby informing the structural interpretation of DEER experiments. Second, to facilitate the interpretation of distance distributions obtained from DEER experiments, we generalized the molecular-simulation method known as Ensemble-Biased Metadynamics. This method, originally designed to generate maximum-entropy structural ensembles consistent with one or more probability distributions, has been modified to account for the uncertainty in those target distributions as dictated by their confidence bands. After careful benchmarks, the proposed techniques have been demonstrated using DEER results from spin-labeled T4 lysozyme. This work is now being extended to allow the determination of confidence bands for distance distributions obtained from the global analysis of multiple data sets. https://lab.vanderbilt.edu/hustedt-lab/software/ https://github.com/Colvars/colvars