Abstract
Double electron-electron resonance (DEER) is now widely utilized to measure distance distributions in the 20-70Å range. DEER is frequently applied to biological systems that have multiple conformational states leading to complex distance distributions. These complex distributions raise issues regarding the best approach to analyze DEER data. A widely used method utilizes a priori background correction followed by Tikhonov regularization. Unfortunately, the underlying assumptions of this approach can impact the analysis. In this chapter, a method of analyzing DEER data is presented that is ideally suited to obtain these complex distance distributions. The approach allows the fitting of raw experimental data without a priori background correction as well as the rigorous determination of uncertainties for all fitting parameters. This same methodological approach can be used for the simultaneous or global analysis of multiple DEER data sets using variable ratios of a common set of components, thus allowing direct correlation of distance components with functionally relevant conformational and biochemical states. Examples are given throughout to highlight this robust fitting approach.