General regularization framework for DEER spectroscopy

Ibáñez, Luis Fábregas

doi:10.1016/j.jmr.2019.01.008

Cited by 18 publications

(10 citation statements)

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“…Regularization methods differ in the choice of the penalty norm R. Tikhonov regularization (Tikhonov, 1963) was the first regularization approach introduced for dipolar data analysis (Bowman et al, 2004;Chiang et al, 2005a;Jeschke et al, 2004). Other approaches such as total variation (TV), Huber regularization, and Osher's Bregman-iterated regularization are beneficial in certain cases (Fábregas Ibáñez and Jeschke, 2019).…”

Section: Non-parametric Distributionsmentioning

confidence: 99%

DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data

2020

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Abstract. Dipolar electron paramagnetic resonance (EPR) spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source software package for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, run global analysis with non-parametric distributions, and use a bootstrapping approach to fully quantify the uncertainty in the analysis.

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Section: Non-parametric Distributionsmentioning

confidence: 99%

DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data

2020

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“…DeerLab provides a flexible regularization framework including all the aforementioned α-selection, non-iterated and iterated regularization methods, as well as a wide selection of solvers. In particular, implementation of Huber and TV regularization is much improved compared to our original study (Fábregas Ibáñez and Jeschke, 2019), such that these now perform similarly to Tikhonov regularization, invalidating the conclusions on the under-performance of Huber and TV regularization from that initial work. Fig.…”

Section: Discussionmentioning

confidence: 67%

DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data

Ibáñez¹,

Jeschke²,

Stoll³

2020

Preprint

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Abstract. Dipolar EPR spectroscopy (DEER and other techniques) enables the structural characterization of macromolecular and biological systems by measurement of distance distributions between unpaired electrons on a nanometer scale. The inference of these distributions from the measured signals is challenging due to the ill-posed nature of the inverse problem. Existing analysis tools are scattered over several applications with specialized graphical user interfaces. This renders comparison, reproducibility, and method development difficult. To remedy this situation, we present DeerLab, an open-source MATLAB toolbox for analyzing dipolar EPR data that is modular and implements a wide range of methods. We show that DeerLab can perform one-step analysis based on separable non-linear least squares, fit dipolar multi-pathway models to multi-pulse DEER data, and run global analysis with parameter-free distributions. Important aspects of uncertainty analysis are discussed as well.

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“…This method has been proven useful for validating conformational heterogeneity when the distance distributions recovered by TIKR are present with multiple peaks. Recently, several methods have been developed to improve the recovery of distance distributions and to provide an estimate of uncertainty in the TIKR results …”

Section: Study Of Activated Bax Protein Structure Is Extremely Challementioning

confidence: 99%

“…[20] Several methods have been developed to improvet he results obtained by TIKR analysis. [21][22][23][24] The resultant distance distributions provide long distantc onstraints (as opposed to relativelys horter distances obtained from NMR) that are useful for the determination of both structure (by averages of the measured distances) and conformational dynamics (by distribution widths andp eak components) in individual proteins or protein complexes. Below,w ep rovide few recent examples that best reveal the great features of pulsed dipolar ESR technique.…”

Section: General Procedures Of Pulsed Dipolar Esr Measurement and Analmentioning

confidence: 99%

“…Among the computer programs published, the DeerAnalysis is the most popular one for the convenient extraction of distance distributions from DEER experimental data . Several methods have been developed to improve the results obtained by TIKR analysis . The resultant distance distributions provide long distant constraints (as opposed to relatively shorter distances obtained from NMR) that are useful for the determination of both structure (by averages of the measured distances) and conformational dynamics (by distribution widths and peak components) in individual proteins or protein complexes.…”

Section: General Procedures Of Pulsed Dipolar Esr Measurement and Analmentioning

confidence: 99%

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Identifying Protein Conformational Dynamics Using Spin‐label ESR

Lai

Kuo

Chiang

2019

Chemistry An Asian Journal

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Spin‐label electron spin resonance (ESR) has emerged as a powerful tool to characterize protein dynamics. One recent advance is the development of ESR for resolving dynamical components that occur or coexist during a biological process. It has been applied to study the complex structural and dynamical aspects of membranes and proteins, such as conformational changes in protein during translocation from cytosol to membrane, conformational exchange between equilibria in response to protein‐protein and protein‐ligand interactions in either soluble or membrane environments, protein oligomerization, and temperature‐ or hydration‐dependent protein dynamics. As these topics are challenging but urgent for understanding the function of a protein on the molecular level, the newly developed ESR methods to capture individual dynamical components, even in low‐populated states, have become a great complement to other existing biophysical tools.

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General regularization framework for DEER spectroscopy

Cited by 18 publications

References 61 publications

DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data

DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data

DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data

Identifying Protein Conformational Dynamics Using Spin‐label ESR

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