Quantifying methyl tunneling induced (de)coherence of nitroxides in glassy ortho-terphenyl at low temperatures.

The low-temperature Hahn echo decay signal of the pyrroline-based nitroxide H-mNOHex in ortho-terphenyl (OTP) shows two contributions on distinct time scales. Tunneling of the nitroxide's methyl groups cause electron spin echo envelope modulation (ESEEM) on a faster time scale compared to the slower matrix-induced decoherence contribution arising from nuclear pair ESEEM. Here we introduce the methyl quantum rotor (MQR) model that describes tunneling ESEEM originating from multiple methyl rotors coupled to the same electron spin. By formulating the MQR model based on a rotation barrier distribution P(V3), we account for the different local environments in a glassy matrix. Using this framework, we determine the methyl groups' rotation barrier distribution from experimental Hahn echo decay/two-pulse ESEEM data by a non-linear fitting approach. The inferred distributions are in good agreement with density functional theory (DFT) calculations of the methyl groups' rotation barriers in the low-temperature regime where tunneling constitutes the dominant methyl proton exchange process. In addition to comparing our results with previous decoherence studies performed on the same spin system, we experimentally confirm the characteristic properties of methyl tunneling by demonstrating that P(V3) is magnetic field independent and predominantly temperature independent between 10 and 50 K. This confirms the assignment of the fast Hahn echo decay contribution to methyl tunneling, showcasing how pulsed EPR sequences can coherently probe this quantum phenomenon for commonly employed nitroxide spin-labels.

Dipolar pathways in multi-spin and multi-dimensional dipolar EPR spectroscopy.

Dipolar electron paramagnetic resonance (EPR) experiments, such as double electron-electron resonance (DEER), measure distributions of nanometer-scale distances between unpaired electrons, which provide valuable information for structural characterization of proteins and other macromolecular systems. We present an extension to our previously published general model based on dipolar pathways valid for multi-dimensional dipolar EPR experiments with more than two spin-1/2 labels. We examine the 4-pulse DEER and TRIER experiments in terms of dipolar pathways and show experimental results confirming the theoretical predictions. This extension to the dipolar pathways model allows the analysis of previously challenging datasets and the extraction of multivariate distance distributions.

Compactness regularization in the analysis of dipolar EPR spectroscopy data.

Dipolar electron paramagnetic resonance (EPR) experiments, such as double electron-electron resonance (DEER), measure distributions of nanometer-scale distances between paramagnetic centers, which are valuable for structural characterization of proteins and other macromolecular systems. One challenge in the least-squares fitting analysis of dipolar EPR data is the separation of the inter-molecular contribution (background) and the intra-molecular contribution. For noisy experimental traces of insufficient length, this separation is not unique, leading to identifiability problems for the background model parameters and the long-distance region of the intra-molecular distance distribution. Here, we introduce a regularization approach that mitigates this by including an additional penalty term in the objective function that is proportional to the variance of the distance distribution and thereby penalizes non-compact distributions. We examine the reliability of this approach statistically on a large set of synthetic data and illustrate it with an experimental example. The results show that the introduction of compactness can improve identifiability.

Neural networks in pulsed dipolar spectroscopy: A practical guide.

This is a methodological guide to the use of deep neural networks in the processing of pulsed dipolar spectroscopy (PDS) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs and paramagnetic metal ions. PDS uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their "robust black box" reputation hides the complexity of their design and training - particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against simulated databases, to discuss network architecture choices, to describe options for handling DEER (double electron-electron resonance) and RIDME (relaxation-induced dipolar modulation enhancement) experiments, and to provide a practical data processing flowchart.

Dipolar pathways in dipolar EPR spectroscopy.

Dipolar electron paramagnetic resonance (EPR) experiments such as double electron-electron resonance (DEER) measure distributions of nanometer-scale distances between unpaired electrons, which provide valuable information for structural characterization of proteins and other macromolecular systems. To determine these distributions from the experimental signal, it is critical to employ an accurate model of the signal. For dilute samples of doubly spin-labeled molecules, the signal is a product of an intramolecular and an intermolecular contribution. We present a general model based on dipolar pathways valid for dipolar EPR experiments with spin-1/2 labels. Our results show that the intramolecular contribution consists of a sum and the intermolecular contribution consists of a product over individual dipolar pathway contributions. We examine several commonly used dipolar EPR experiments in terms of dipolar pathways and show experimental results confirming the theoretical predictions. This multi-pathway model makes it possible to analyze a wide range of dipolar EPR experiments within a single theoretical framework.

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

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.

Optimal background treatment in dipolar spectroscopy.

Treatment of the background in dipolar EPR spectroscopy signals is a critical processing step for the recovery of the underlying distance distributions. Here we present new mathematical considerations that show pitfalls of background subtraction and division. In order to overcome these problems we propose an improved background treatment approach. We show, empirically, that this new method outperforms the established ones and analyze the established practice of post-correction signal truncation, as well as the influence of moderate background-fit errors, on accuracy of distance distributions.

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

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 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.

Non-uniform HYSCORE: Measurement, processing and analysis with Hyscorean.

Non-uniform sampling (NUS) provides a considerable reduction of measurement time especially for multi-dimensional experiments. This comes at the cost of additional signal processing steps to reconstruct the complete signal from the experimental data points. Despite being routinely employed in NMR for many experiments, EPR applications have not benefited from NUS due to the lack of a straightforward implementation to perform NUS in common commercial spectrometers. In this work we present a novel method to perform NUS HYSCORE experiments on commercial Bruker EPR spectrometers, along with a benchmark of modern reconstruction methods, and new processing software tools for NUS HYSCORE signals. All of this comes in the form of a free-software package: Hyscorean. Experimental NUS spectra are measured and processed with this package using different reconstruction methods and compared to their uniform sampled counterparts, thereby showcasing the method's potential for EPR spectroscopy.

General regularization framework for DEER spectroscopy.

Tikhonov regularization is the standard processing technique for the inversion of double electron-electron resonance (DEER) data to distance distributions without assuming a parametrized model. In other fields it has been surpassed by modern regularization methods. We analyze such alternative regularization methods based on the Tikhonov, total variation (TV) and Huber penalties with and without the use of Bregman iterations. For this, we provide a general mathematical framework and its open-source software implementation. We extend an earlier approach by Edwards and Stoll for the selection of an optimal regularization parameter to all of these penalties and use their big test data set of noisy DEER traces with known ground truth for assessment. The results indicate that regularization methods based on Bregman iterations provide an improvement upon Tikhonov regularization in recognizing features and recovering distribution width at moderate signal-to-noise ratio, provided that noise variance is known. Bregman-iterative methods are robust with respect to the method used in the choice of regularization parameter.