DD
DD is a MATLAB program designed to provide a convenient GUI-based tool for analyzing single DEER data sets. DD is based on the original GLADD program that was developed to provide a flexible framework for the global analysis of data from multiple DEER experiments. GLADD is being replaced by GLADDvu, a GUI-based tool for the global analysis of DEER data. The main features of both DD and GLADDvu are:
- Minimal processing of data occurs prior to fitting,. A priori background correction is not used. Data is fit to a function which includes both the background factor and the intra-object spin interactions.
- The background factor due to inter-object spin interactions is fit as an exponential, a stretched exponential, or using a model which accounts for excluded volume effects.
- The distance distribution for the intra-object spin interactions is modeled as a sum of Gaussians or other functions.
- The Bayesian Information Criterion (BIC) is used for model selection to determine the optimal model.
- Estimated uncertainties for all fit parameters and for the best‑fit distance distribution are automatically provided.
Downloads:
- DD Version 7C (requires MATLAB license, Optimization and Global Optimization Toolboxes are highly recommended) (last updated 4/11/2019)
- DD Version 7B (requires MATLAB license, Optimization and Global Optimization Toolboxes are highly recommended) (last updated 8/16/2018)
- DD Version 7 (requires MATLAB license, Optimization and Global Optimization Toolboxes are highly recommended) (last updated 7/22/2018)
- DD Version 6C (requires MATLAB license) (last updated 07/30/2017)
older versions
- DD Version 4 (requires MATLAB license) (last updated 05/31/2016)
- DD Version 2 (requires MATLAB license) (last updated 05/31/2016)
- DD Version 2 manual
- Example 1 data
- Example 2 data
References:
- Brandon et al., “The global analysis of DEER data”. J. Magn. Reson. 218: 93-104 (2012).
- Stein et al., “A straightforward approach to the analysis of DEER data”. Method. Enzym. 563. 531-67 (2015)
- Hustedt et al., “Confidence Analysis of DEER Data and its Structural Interpretation with Ensemble-Biased Metadynamics” (2018)