ArticleThe journal of physical chemistry. A2020
Bayesian Probabilistic Analysis of DEER Spectroscopy Data Using Parametric Distance Distribution Models.
Article in The journal of physical chemistry. A, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advances in EPR Approaches for Studying Structural Properties of Membrane Proteins.Magnetic resonance in chemistry : MRC · 2026Review
- Protein Modeling with DEER Spectroscopy.Annual review of biophysics · 2025Review
- Bayesian Probabilistic Inference of Nonparametric Distance Distributions in DEER Spectroscopy.The journal of physical chemistry. A · 2024Article
- Measuring conformational equilibria in allosteric proteins with time-resolved tmFRET.Biophysical journal · 2024Article
- Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms.Applied magnetic resonance · 2024Article
- Measuring conformational equilibria in allosteric proteins with time-resolved tmFRET.bioRxiv : the preprint server for biology · 2024Article
- Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum.Foods (Basel, Switzerland) · 2022Article
- Reweighting methods for elucidation of conformation ensembles of proteins.Current opinion in structural biology · 2022Review
- Dipolar pathways in dipolar EPR spectroscopy.Physical chemistry chemical physics : PCCP · 2022Article
- Article
- 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
3 authors.
Funding
Abstract
Double electron-electron resonance (DEER) spectroscopy measures distance distributions between spin labels in proteins, yielding important structural and energetic information about conformational landscapes. Analysis of an experimental DEER signal in terms of a distance distribution is a nontrivial task due to the ill-posed nature of the underlying mathematical inversion problem. This work introduces a Bayesian probabilistic inference approach to analyze DEER data, using a multi-Gauss mixture model for the distance distribution. The method uses Markov chain Monte Carlo (MCMC) sampling to determine a posterior probability distribution over model parameter space. This distribution contains all the information available from the data, including a full quantification of the uncertainty about the parameters. The corresponding uncertainty about the distance distribution is captured via an ensemble of posterior predictive distributions. Several synthetic examples illustrate the method. An experimental example shows the importance of model checking and comparison using residual analysis and Bayes factors. Overall, the Bayesian approach allows for more robust inference about protein conformations from DEER spectroscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.