Neural networks in pulsed dipolar spectroscopy: A practical guide

Keeley, Jake; Choudhury, Tajwar; Galazzo, Laura; Bordignon, Enrica; Feintuch, Akiva; Goldfarb, Daniella; Russell, Hannah; Taylor, Michael J.; Lovett, Janet E.; Eggeling, Andrea; Ibáñez, Luis Fábregas; Keller, Katharina; Yulikov, Maxim; Jeschke, Gunnar; Kuprov, Ilya

doi:10.1016/j.jmr.2022.107186

Cited by 22 publications

(22 citation statements)

References 66 publications

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“…In order to obtain further insight, all data sets were additionally analyzed by neural network analysis with DEERNet 2.0, by multi-Gauss fitting with DD, by one-step Tikhonov regularization with automatic selection of the regularization parameter by the Akaike information criterion (AIC) with DeerLab and by a comparative analysis that compares neural network analysis and regularization (SI, section S2; Figures S11–S14). In general, all approaches yield similar distance distributions, but differ considerably in their uncertainty estimates, which do not cover the full variation between laboratories in the case of DEERNet and exceed this variation substantially in the cases of multi-Gauss fitting.…”

Section: Peldor/deer Measurements and Distance Distributionsmentioning

confidence: 99%

Benchmark Test and Guidelines for DEER/PELDOR Experiments on Nitroxide-Labeled Biomolecules

et al. 2021

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Distance distribution information obtained by pulsed dipolar EPR spectroscopy provides an important contribution to many studies in structural biology. Increasingly, such information is used in integrative structural modeling, where it delivers unique restraints on the width of conformational ensembles. In order to ensure reliability of the structural models and of biological conclusions, we herein define quality standards for sample preparation and characterization, for measurements of distributed dipole–dipole couplings between paramagnetic labels, for conversion of the primary time-domain data into distance distributions, for interpreting these distributions, and for reporting results. These guidelines are substantiated by a multi-laboratory benchmark study and by analysis of data sets with known distance distribution ground truth. The study and the guidelines focus on proteins labeled with nitroxides and on double electron–electron resonance (DEER aka PELDOR) measurements and provide suggestions on how to proceed analogously in other cases.

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Section: Peldor/deer Measurements and Distance Distributionsmentioning

confidence: 99%

Benchmark Test and Guidelines for DEER/PELDOR Experiments on Nitroxide-Labeled Biomolecules

et al. 2021

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“…The extent to which the packages are able to deal with different background forms has not been investigated more thoroughly in this work since there are a wide range of possible backgrounds, and the user is reminded to be cautious when analyzing non-standard DEER data in whatever form that takes. We note that the RIDME (relaxation induced dipolar modulation enhancement) experiment, which like DEER measures dipolar coupling frequency, suffers from a, to date, not well predicted background function and that DeerNet (and ComparativeDeerAnalyzer) has recently been expanded to include RIDMENets ( Milikisyants et al, 2009 ; Keller et al, 2019 ; Ritsch et al, 2019 ; Keeley et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…In theory the distances between pairwise interactions can be found from analysis of the Fourier transform of the DEER time trace, but in practice this is inaccurate, especially if there is a distribution of distances between the centers. There are a range of free-to-download software packages for providing the distance distributions and the mathematics of these fall broadly into one or more of Tikhonov regularisation, model based approaches, or trained deep-neural networks ( Jeschke et al, 2006 ; Brandon et al, 2012 ; Fábregas Ibáñez et al, 2020 ; Keeley et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

DEER Data Analysis Software: A Comparative Guide

Russell

Cura

Lovett

2022

Front. Mol. Biosci.

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Pulsed dipolar electron paramagnetic resonance (PDEPR) spectroscopy experiments measure the dipolar coupling, and therefore nanometer-scale distances and distance distributions, between paramagnetic centers. Of the family of PDEPR experiments, the most commonly used pulsed sequence is four-pulse double electron resonance (DEER, also known as PELDOR). There are several ways to analyze DEER data to extract distance distributions, and this may appear overwhelming at first. This work compares and reviews six of the packages, and a brief getting started guide for each is provided.

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“…For background correction we used a dimensionality of 3, corresponding to a homogenoeus three-dimensional distribution of complexes in the sample as expected for soluble proteins. Automated comparative analysis computes distance distributions by neural network analysis with DeerNet (86) and Tikhonov regularization (87) with DeerLab in a single step with background correction (88) and provides 95% confidence intervals for both distributions. Unless otherwise indicated, we report the mean of the two distributions and confidence intervals that include uncertainty due to model bias.…”

Section: Methodsmentioning

confidence: 99%

“…Samples with iodoacetamido proxyl spin label are indicated by “IAP”. Distance distributions computed by neural network analysis with DeerNet (86) are indicated.…”

Section: Fig S1mentioning

confidence: 99%

Integrative solution structure of a PTBP1-viral IRES complex reveals strong compaction and ordering with residual conformational flexibility

Dorn

Gmeiner

Vries

et al. 2022

Preprint

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RNA-binding proteins (RBPs) are crucial regulators of gene expression and often comprise well-defined domains interspersed by flexible, intrinsically disordered regions. The structure determination of ribonucleoprotein complexes involving such RBPs is not common practice and requires integrative structural modeling approaches due to the fact that they often do not form a single stable globular state. Here, we integrate data from magnetic resonance, mass spectrometry, and small angle scattering to determine the solution structure of the polypyrimidine-tract binding protein 1 (PTBP1 also called hnRNP I) bound to an RNA which is part of the internal ribosome entry site (IRES) of the encephalomyocarditis virus (EMCV). PTBP1 binding to this IRES element enhances translation of the viral RNA. The determined structural ensemble reveals that both RNA and protein experience a strong compaction upon complex formation, get ordered but still maintain a substantial conformational flexibility. The C-terminal RNA recognition motif (RRM4) of PTBP1 rigidifies the complex by binding a single-strand RNA linker and, in turn, is essential for IRES-mediated translation. PTBP1 acts as an RNA chaperone for the IRES, by ordering the RNA into a few discrete conformations that expose the RNA stems to the outer surface of the RNP complex for subsequent interactions with the translation machinery. The conformational diversity within this structural ensemble is likely common among RNP complexes and important for their functionality. The presented approach opens the possibility to characterize heterogeneous RNP structures at atomic level.

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Neural networks in pulsed dipolar spectroscopy: A practical guide

Cited by 22 publications

References 66 publications

Benchmark Test and Guidelines for DEER/PELDOR Experiments on Nitroxide-Labeled Biomolecules

Benchmark Test and Guidelines for DEER/PELDOR Experiments on Nitroxide-Labeled Biomolecules

DEER Data Analysis Software: A Comparative Guide

Integrative solution structure of a PTBP1-viral IRES complex reveals strong compaction and ordering with residual conformational flexibility

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