Optimal Tikhonov regularization for DEER spectroscopy.

Area of Science:

• Biophysics
• Spectroscopy
• Computational Chemistry

Background:

• Tikhonov regularization is standard for DEER spectroscopy data analysis.
• Selecting the regularization parameter (α) and operator (L) is crucial but challenging.
• Current L-curve methods may not be optimal for DEER data analysis.

Purpose of the Study:

• To systematically evaluate various α selection methods and regularization operators for DEER data.
• To identify superior methods for accurate distance distribution recovery from noisy DEER traces.
• To compare performance against commonly used L-curve techniques.

Main Methods:

• Generation of over half a million synthetic noisy DEER traces from T4 lysozyme crystal structures.
• In silico double labeling with MTSSL spin labels to create model distance distributions.
• Analysis of multiple α selection strategies and regularization operators (L).

Main Results:

• Several α selection methods demonstrated strong performance in recovering model distance distributions.
• Akaike information criterion and generalized cross-validation (with derivative operators) were particularly effective.
• These methods significantly outperformed traditional L-curve approaches.

Conclusions:

• Akaike information criterion and generalized cross-validation offer improved accuracy for DEER data analysis.
• The choice of regularization parameter selection and operator significantly impacts distance distribution recovery.
• These findings provide guidance for optimizing DEER data analysis in biophysical studies.