ArticleJournal of magnetic resonance (San Diego, Calif. : 1997)2016
A Bayesian approach to quantifying uncertainty from experimental noise in DEER spectroscopy.
Article in Journal of magnetic resonance (San Diego, Calif. : 1997), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Q-Band Double Quantum Coherence ESR for Sensitive Nitroxide-Based Distance Measurements.The journal of physical chemistry. B · 2025Article
- Localized Reconstruction of Multimodal Distance Distribution from DEER Data of Biopolymers.bioRxiv : the preprint server for biology · 2025Article
- Expanded Functionality and Portability for the Colvars Library.The journal of physical chemistry. B · 2024Article
- Bayesian Probabilistic Inference of Nonparametric Distance Distributions in DEER Spectroscopy.The journal of physical chemistry. A · 2024Article
- The transport activity of the multidrug ABC transporter BmrA does not require a wide separation of the nucleotide-binding domains.The Journal of biological chemistry · 2024Article
- Cross-validation of distance measurements in proteins by PELDOR/DEER and single-molecule FRET.Nature communications · 2022Article
- Dipolar pathways in dipolar EPR spectroscopy.Physical chemistry chemical physics : PCCP · 2022Article
- Representativeness of survey participants in relation to mental disorders: a linkage between national registers and a population-representative survey.International journal of population data science · 2022Article
- OOP-ESEEM Spectroscopy: Accuracies of Distances of Spin-Correlated Radical Pairs in Biomolecules.Frontiers in molecular biosciences · 2022Review
- Benchmark Test and Guidelines for DEER/PELDOR Experiments on Nitroxide-Labeled Biomolecules.Journal of the American Chemical Society · 2021Article
- Protein functional dynamics from the rigorous global analysis of DEER data: Conditions, components, and conformations.The Journal of general physiology · 2021Article
- Statistical analysis of ENDOR spectra.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Methodology for rigorous modeling of protein conformational changes by Rosetta using DEER distance restraints.PLoS computational biology · 2021Article
- Studies of transmembrane peptides by pulse dipolar spectroscopy with semi-rigid TOPP spin labels.European biophysics journal : EBJ · 2021Review
- DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles.PLoS computational biology · 2021Article
- A Two-Armed Probe for In-Cell DEER Measurements on Proteins*.Chemistry (Weinheim an der Bergstrasse, Germany) · 2020Article
- Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy.The journal of physical chemistry. B · 2020Article
- Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models.The journal of physical chemistry. A · 2020Article
- Measurement of Angstrom to Nanometer Molecular Distances withAngewandte Chemie (International ed. in English) · 2020Article
- DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data.Magnetic resonance (Gottingen, Germany) · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Double Electron-Electron Resonance (DEER) spectroscopy is a solid-state pulse Electron Paramagnetic Resonance (EPR) experiment that measures distances between unpaired electrons, most commonly between protein-bound spin labels separated by 1.5-8nm. From the experimental data, a distance distribution P(r) is extracted using Tikhonov regularization. The disadvantage of this method is that it does not directly provide error bars for the resulting P(r), rendering correct interpretation difficult. Here we introduce a Bayesian statistical approach that quantifies uncertainty in P(r) arising from noise and numerical regularization. This method provides credible intervals (error bars) of P(r) at each r. This allows practitioners to answer whether or not small features are significant, whether or not apparent shoulders are significant, and whether or not two distance distributions are significantly different from each other. In addition, the method quantifies uncertainty in the regularization parameter.
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