The global analysis of DEER data

jbnjdhjbdkf ► DEER data can be analyzed routinely without a priori background correction. ► For molecules or assemblies of sufficient size, the true background signal may deviate significantly from an exponential. ► Rigorous determination of confidence intervals is used to compare results from different data sets. ► Multiple DEER data sets can be combined into a single global analysis. Double Electron–Electron Resonance (DEER) has emerged as a powerful technique for measuring long range distances and distance distributions between paramagnetic centers in biomolecules. This information can then be used to characterize functionally relevant structural and dynamic properties of biological molecules and their macromolecular assemblies. Approaches have been developed for analyzing experimental data from standard four-pulse DEER experiments to extract distance distributions. However, these methods typically use an a priori baseline correction to account for background signals. In the current work an approach is described for direct fitting of the DEER signal using a model for the distance distribution which permits a rigorous error analysis of the fitting parameters. Moreover, this approach does not require a priori background correction of the experimental data and can take into account excluded volume effects on the background signal when necessary. The global analysis of multiple DEER data sets is also demonstrated. Global analysis has the potential to provide new capabilities for extracting distance distributions and additional structural parameters in a wide range of studies.