Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions

Protein Modeling with DEER Spectroscopy

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

Characterization of conformationally heterogeneous proteins by electron paramagnetic resonance spectroscopy

The Anfinsen paradigm of representing a protein by a single conformer is challenged by the uncertainty predictions that come with AlphaFold models, which suggest a greater extent of disorder. Characterization of such conformation heterogeneity requires experimental approaches that do not depend on long-range order. Site-directed spin labeling (SDSL) coupled with electron paramagnetic resonance (EPR) spectroscopy is such an approach. The double electron-electron resonance (DEER) technique can access site-pair distance distributions in the 15-100 Å range, directly informing on ensemble width. SDSL-EPR can be applied in cellular environments, and recent work indicates that protein disorder is even more pervasive than predicted by AlphaFold. This suggests that the Anfinsen paradigm should be replaced by an ensemble paradigm.

RIDME Spectroscopy: New Topics Beyond the Determination of Electron Spin-Spin Distances

Relaxation-induced dipolar modulation enhancement (RIDME) is a pulse EPR experiment originally designed to determine distances between spin labels. However, RIDME has several features that make it an efficient tool in a number of "nonconventional" applications, away from the original purpose of this pulse experiment. RIDME appears to be an interesting experiment to probe longitudinal electron spin dynamics, e.g., in relation to qubits research, to probe distributions of exchange couplings, useful for the design of molecular magnets, and to determine important details of electron spin interactions with the nuclear spin bath, which is related to the dynamic nuclear polarization and soft materials research. We also anticipate interesting applications of RIDME in the structural biology of biopolymers as well as their interactions, aggregation, and phase separation. It is not excluded that in the near future such "nonconventional" topics could grow in number and evolve into the main application area of RIDME.

Quantification of Distributions of Local Proton Concentrations in Heterogeneous Soft Matter and Non-Anfinsen Biomacromolecules

A new method to quantitatively analyze heterogeneous distributions of local proton densities around paramagnetic centers in unstructured and weakly structured biomacromolecules and soft matter is introduced, and its feasibility is demonstrated on aqueous solutions of stochastically spin-labeled polysaccharides. This method is based on the pulse EPR experiment ih-RIDME (intermolecular hyperfine relaxation-induced dipolar modulation enhancement). Global analysis of a series of RIDME traces allows for a mathematically stable transformation of the time-domain data to the distribution of local proton concentrations. Two pulse sequences are proposed and tested, which combine the ih-RIDME block and the double-electron-electron resonance (DEER) experiment. Such experiments can be potentially used to correlate the local proton concentration with the macromolecular chain conformation. We anticipate an application of this approach in studies of intrinsically disordered proteins, biomolecular aggregates, and biomolecular condensates.

Ensemble structure of the N-terminal domain (1-267) of FUS in a biomolecular condensate

Solutions of some proteins phase separate into a condensed state of high protein concentration and a dispersed state of low concentration. Such behavior is observed in living cells for a number of RNA-binding proteins that feature intrinsically disordered domains. It is relevant for cell function via the formation of membraneless organelles and transcriptional condensates. On a basic level, the process can be studied in vitro on protein domains that are necessary and sufficient for liquid-liquid phase separation (LLPS). We have performed distance distribution measurements by electron paramagnetic resonance for 13 sections in an N-terminal domain (NTD) construct of the protein fused in sarcoma (FUS), consisting of the QGSY-rich domain and the RGG1 domain, in the denatured, dispersed, and condensed state. Using 10 distance distribution restraints for ensemble modeling and three such restraints for model validation, we have found that FUS NTD behaves as a random-coil polymer under good-solvent conditions in both the dispersed and condensed state. Conformation distribution in the biomolecular condensate is virtually indistinguishable from the one in an unrestrained ensemble, with the latter one being based on only residue-specific Ramachandran angle distributions. Over its whole length, FUS NTD is slightly more compact in the condensed than in the dispersed state, which is in line with the theory for random coils in good solvent proposed by de Gennes, Daoud, and Jannink. The estimated concentration in the condensate exceeds the overlap concentration resulting from this theory. The QGSY-rich domain is slightly more extended, slightly more hydrated, and has slightly higher propensity for LLPS than the RGG1 domain. Our results support previous suggestions that LLPS of FUS is driven by multiple transient nonspecific hydrogen bonding and π-sp2 interactions.

Protein ensemble modeling and analysis with MMMx

Proteins, especially of eukaryotes, often have disordered domains and may contain multiple folded domains whose relative spatial arrangement is distributed. The MMMx ensemble modeling and analysis toolbox (https://github.com/gjeschke/MMMx) can support the design of experiments to characterize the distributed structure of such proteins, starting from AlphaFold2 predictions or folded domain structures. Weak order can be analyzed with reference to a random coil model or to peptide chains that match the residue-specific Ramachandran angle distribution of the loop regions and are otherwise unrestrained. The deviation of the mean square end-to-end distance of chain sections from their average over sections of the same sequence length reveals localized compaction or expansion of the chain. The shape sampled by disordered chains is visualized by superposition in the principal axes frame of their inertia tensor. Ensembles of different sizes and with weighted conformers can be compared based on a similarity parameter that abstracts from the ensemble width.

Confidence limits in pulse dipolar EPR spectroscopy: estimates for individual measurements

The distribution of inter-label distances obtained by electron paramagnetic resonance (EPR) pulse dipolar spectroscopies (PDS), such as DEER aka PELDOR, gives a valuable characterization of structure on the nanometer scale. The impact of random experimental noise on such experiments is examined for three independent methods for analysing PDS data: the model-free method with Tikhonov regularization, model-free with Mellin-transformation, and a model-based method. All three methods show negative bias for the mean distance and positive bias for the distribution width. Both biases grow with increasing noise levels. The estimated confidence bands and the uncertainties obtained from a single experimental measurement by the standard bootstrapping or χ2-surface scanning approaches are inconsistent and can exclude the true distance distribution. Yet, both approaches can provide quite valuable support for hypothesis testing in PDS studies.

Pulse EPR spectroscopy and molecular modeling reveal the origins of the local heterogeneity of dietary fibers

Optimizing human diet by including dietary fibers would be more efficient when the fibers' chain interactions with other molecules are understood in depth. Thereby, it is important to develop methods for characterizing the fiber chain to be able to monitor its structural alterations upon intermolecular interactions. Here, we demonstrate the utility of the electron paramagnetic resonance (EPR) spectroscopy, complemented by simulations in probing the atomistic details of the chain conformations for spin-labeled fibers. Barley β-glucan, a native polysaccharide with linear chain, was utilized as a test fiber system to demonstrate the technique's capabilities. Pulse dipolar EPR data show good agreement with results of the fiber chain modeling, revealing sinuous chain conformations and providing polymer shape descriptors: the gyration tensor, spin-spin distance distribution function, and information about proton density near the spin probe. Results from EPR measurements point to the fiber aggregation in aqueous solution, which agrees with the results of the dynamic light scattering. We propose that the combination of pulse EPR measurements with modeling can be a perfect experimental tool for in-depth structural investigation of dietary fibers and their interaction under such conditions, and that the presented methodology can be extended to other weakly ordered or disordered macromolecules.

Phase Separation of Heterogeneous Nuclear Ribonucleoprotein A1 upon Specific RNA-Binding Observed by Magnetic Resonance

Interaction of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) with specific single-stranded RNA and its relation to liquid-liquid phase separation (LLPS) were studied in vitro by magnetic resonance based on site-directed spin labelling. An ensemble model of dispersed hnRNP A1 in the absence of RNA was derived from distance distributions between spin labelled sites and small angle X-ray scattering. This model revealed a compact state of the low-complexity domain and its interaction with the RNA recognition motifs. Paramagnetic relaxation enhancement NMR spectroscopy confirmed this interaction. Addition of RNA to dispersed hnRNP A1 induced liquid-droplet formation. Such LLPS depended on RNA concentration and sequence, with continuous wave EPR spectroscopy showing an influence of RNA point mutations on local protein dynamics. We propose that an interplay of sequence-specific RNA binding and LLPS contributes to regulation of specific RNA segregation during stress response.

Integrative ensemble modeling of proteins and their complexes with distance distribution restraints

Many proteins and protein complexes exhibit regions that are intrinsically disordered. Whereas an arsenal of techniques exists to characterize structured proteins or protein regions, characterization of the vast conformational space occupied by intrinsically disordered regions remains a challenging task due the ensemble-averaging nature of many techniques that provide mean value restraints. More representative information can be gained in the form of distribution restraints, such as EPR-derived distance distributions. Previously we developed the ensemble modeling tool MMM, where we partition the macromolecule into structured and unstructured domains and utilize an integrative structural approach with a focus on EPR-derived distance restraints. Here we present the successor program of MMM: MMMx. All the modeling functionality was ported to MMMx and is now accessed by a uniform script format, allowing to combine the different modules at will to modeling pipelines. During the conception of MMMx many of the tools were improved or updated. We discuss the general functionality of MMMx and its modules, and illustrate some of the modeling tools by application examples.

Dynamical interplay between the human high-affinity copper transporter hCtr1 and its cognate metal ion

Abnormal cellular copper levels have been clearly implicated in genetic diseases, cancer, and neurodegeneration. Ctr1, a high-affinity copper transporter, is a homotrimeric integral membrane protein that provides the main route for cellular copper uptake. Together with a sophisticated copper transport system, Ctr1 regulates Cu(I) metabolism in eukaryotes. Despite its pivotal role in normal cell function, the molecular mechanism of copper uptake and transport via Ctr1 remains elusive. In this study, electron paramagnetic resonance (EPR), UV-visible spectroscopy, and all-atom simulations were employed to explore Cu(I) binding to full-length human Ctr1 (hCtr1), thereby elucidating how metal binding at multiple distinct sites affects the hCtr1 conformational dynamics. We demonstrate that each hCtr1 monomer binds up to five Cu(I) ions and that progressive Cu(I) binding triggers a marked structural rearrangement in the hCtr1 C-terminal region. The observed Cu(I)-induced conformational remodeling suggests that the C-terminal region may play a dual role, serving both as a channel gate and as a shuttle mediating the delivery of copper ions from the extracellular hCtr1 selectivity filter to intracellular metallochaperones. Our findings thus contribute to a more complete understanding of the mechanism of hCtr1-mediated Cu(I) uptake and provide a conceptual basis for developing mechanism-based therapeutics for treating pathological conditions linked to de-regulated copper metabolism.

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.