Open access
Datum
2022-06Typ
- Journal Article
Abstract
Dipolar electron paramagnetic resonance (EPR) experiments, such as double electron–electron resonance (DEER), measure distributions of nanometer-scale distances between paramagnetic centers, which are valuable for structural characterization of proteins and other macromolecular systems. One challenge in the least-squares fitting analysis of dipolar EPR data is the separation of the inter-molecular contribution (background) and the intra-molecular contribution. For noisy experimental traces of insufficient length, this separation is not unique, leading to identifiability problems for the background model parameters and the long-distance region of the intra-molecular distance distribution. Here, we introduce a regularization approach that mitigates this by including an additional penalty term in the objective function that is proportional to the variance of the distance distribution and thereby penalizes non-compact distributions. We examine the reliability of this approach statistically on a large set of synthetic data and illustrate it with an experimental example. The results show that the introduction of compactness can improve identifiability. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000544001Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Journal of Magnetic ResonanceBand
Seiten / Artikelnummer
Verlag
ElsevierThema
Electron paramagnetic resonance; Dipolar EPR spectroscopy; Pulse dipolar spectroscopy; Identifiablity; Regularization; Profile likelihood; Compactness; DEER; PELDOR; Distance distribution; Data analysisOrganisationseinheit
03810 - Jeschke, Gunnar / Jeschke, Gunnar
Förderung
ETH-35 18-2 - Optimal regularization in computation of one- and two-dimensional distance distributions from pulsed dipolar spectroscopy data (ETHZ)
Zugehörige Publikationen und Daten
Is part of: https://doi.org/10.3929/ethz-b-000585847