DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data

Ibáñez, Luis Fábregas; Jeschke, Gunnar; Stoll, Stefan

doi:10.5194/mr-2020-13

Cited by 10 publications

(18 citation statements)

References 54 publications

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“…The fraction of overlapping area (green) is a measure for agreement of the ensemble with the restraint. Primary data were taken from the thesis of Christoph Gmeiner 19 on the PTBP1/EMCV‐IRES DtoF complex and reprocessed with DeerLab 20 . The colored bar below the distribution encodes reliability of the distribution.…”

Section: Rigiflex Pipelinementioning

confidence: 99%

MMM: Integrative ensemble modeling and ensemble analysis

Jeschke

2020

Protein Science

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Proteins and their complexes can be heterogeneously disordered. In ensemble modeling of such systems with restraints from several experimental techniques the following problems arise: (a) integration of diverse restraints obtained on different samples under different conditions; (b) estimation of a realistic ensemble width; (c) sufficient sampling of conformational space; (d) representation of the ensemble by an interpretable number of conformers; (e) recognition of weak order with site resolution. Here, I introduce several tools that address these problems, focusing on utilization of distance distribution information for estimating ensemble width. The RigiFlex approach integrates such information with high‐resolution structures of ordered domains and small‐angle scattering data. The EnsembleFit module provides moderately sized ensembles by fitting conformer populations and discarding conformers with low population. EnsembleFit balances the loss in fit quality upon combining restraint subsets from different techniques. Pair correlation analysis for residues and local compaction analysis help in feature detection. The RigiFlex pipeline is tested on data simulated from the structure 70 kDa protein‐RNA complex RsmE/RsmZ. It recovers this structure with ensemble width and difference from ground truth both being on the order of 4.2 Å. EnsembleFit reduces the ensemble of the proliferating‐cell‐nuclear‐antigen‐associated factor p15PAF from 4,939 to 75 conformers while maintaining good fit quality of restraints. Local compaction analysis for the PaaA2 antitoxin from E. coli O157 revealed correlations between compactness and enhanced residual dipolar couplings in the original NMR restraint set.

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Section: Rigiflex Pipelinementioning

confidence: 99%

MMM: Integrative ensemble modeling and ensemble analysis

Jeschke

2020

Protein Science

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“…This procedure was implemented in DeerAnalysis by Jeschke and coworkers (Jeschke et al 2006 ), the software used for all our low-field studies. Recently, several methods were reported for treating noisy experimental dipolar spectra and to quantify uncertainties of distance measurements (Edwards and Stoll 2016 ; Fábregas Ibáñez and Jeschke 2019 , 2020 ; Fábregas Ibáñez et al 2020 ; Srivastava and Freed 2017 , 2019 ; Worswick et al 2018 ). These methods aim at establishing a good practice of signal post-processing and evaluate the influence of moderate background-fit errors on accuracy of distance determination.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Studies of transmembrane peptides by pulse dipolar spectroscopy with semi-rigid TOPP spin labels

2021

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Electron paramagnetic resonance (EPR)-based pulsed dipolar spectroscopy measures the dipolar interaction between paramagnetic centers that are separated by distances in the range of about 1.5–10 nm. Its application to transmembrane (TM) peptides in combination with modern spin labelling techniques provides a valuable tool to study peptide-to-lipid interactions at a molecular level, which permits access to key parameters characterizing the structural adaptation of model peptides incorporated in natural membranes. In this mini-review, we summarize our approach for distance and orientation measurements in lipid environment using novel semi-rigid TOPP [4-(3,3,5,5-tetramethyl-2,6-dioxo-4-oxylpiperazin-1-yl)-L-phenylglycine] labels specifically designed for incorporation in TM peptides. TOPP labels can report single peak distance distributions with sub-angstrom resolution, thus offering new capabilities for a variety of TM peptide investigations, such as monitoring of various helix conformations or measuring of tilt angles in membranes. Graphical Abstract

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“…Electron-electron distance distributions extracted from DEER experiments have previously routinely been compared with distributions predicted using the RLA implemented in the Matlab-based MMM modeling toolbox (http://www.epr.ethz.ch/software) [10,33]. Since MMM intrinsically operates on single structures, we and others had to resort to wrapper scripts to compute distance distributions of large ensembles, such as MD trajectories [3,22,34].…”

Section: Predicting the Deer Signal From Structural Ensemblesmentioning

confidence: 99%

DEER-PREdict: Software for Efficient Calculation of Spin-Labeling EPR and NMR Data from Conformational Ensembles

Tesei

Martins

Kunze

et al. 2020

Preprint

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Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound covalently to the protein. DEER-PREdict has been designed to operate efficiently on large conformational ensembles, such as those generated by molecular dynamics simulation, to facilitate the validation or refinement of molecular models as well as the interpretation of experimental data. The performance and accuracy of the software is demonstrated with experimentally characterized protein systems: HIV-1 protease, T4 Lysozyme and Acyl-CoA-binding protein. DEER-PREdict is open source (GPLv3) and available at https://github.com/KULL-Centre/DEERpredict and as a Python PyPI package https://pypi.org/project/DEERPREdict.

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DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data

Cited by 10 publications

References 54 publications

MMM: Integrative ensemble modeling and ensemble analysis

MMM: Integrative ensemble modeling and ensemble analysis

Studies of transmembrane peptides by pulse dipolar spectroscopy with semi-rigid TOPP spin labels

DEER-PREdict: Software for Efficient Calculation of Spin-Labeling EPR and NMR Data from Conformational Ensembles

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