Confidence limits in pulse dipolar EPR spectroscopy: estimates for individual measurements

Area of Science:

• Biophysics
• Analytical Chemistry
• Spectroscopy

Background:

• Electron paramagnetic resonance (EPR) pulse dipolar spectroscopies (PDS), including DEER/PELDOR, provide nanometer-scale structural insights.
• Accurate analysis of PDS data is crucial for understanding molecular conformations and interactions.

Purpose of the Study:

• To investigate the impact of experimental noise on PDS data analysis.
• To evaluate the performance of different analytical methods under noisy conditions.
• To assess the reliability of uncertainty estimation techniques in PDS.

Main Methods:

• Analysis of PDS data using three distinct methods: model-free with Tikhonov regularization, model-free with Mellin-transformation, and a model-based approach.
• Simulation of experimental noise to assess its effect on distance distributions.
• Evaluation of bootstrapping and chi-squared surface scanning for uncertainty quantification.

Main Results:

• All three analytical methods exhibit negative bias in mean distance and positive bias in distribution width, which worsen with increasing noise.
• Estimated confidence bands and uncertainties from bootstrapping and chi-squared scanning are often inconsistent and may exclude true distributions.
• Despite limitations, these uncertainty approaches can still support hypothesis testing in PDS studies.

Conclusions:

• Experimental noise significantly compromises the accuracy of nanometer-scale distance measurements derived from PDS.
• Standard uncertainty quantification methods require careful consideration and may not be universally applicable for PDS data.
• Further development of robust analytical and uncertainty estimation techniques is needed for reliable PDS data interpretation.