Differentiating between Label and Protein Conformers in Pulsed Dipolar EPR Spectroscopy with the dHis-Cu2+ (NTA) Motif

Impact of Cellular Crowding on Protein Structural Dynamics Investigated by EPR Spectroscopy

The study of how the intracellular medium influences protein structural dynamics and protein-protein interactions is a captivating area of research for scientists aiming to comprehend biomolecules in their native environment. As the cellular environment can hardly be reproduced in vitro, direct investigation of biomolecules within cells has attracted growing interest in the past two decades. Among magnetic resonances, site-directed spin labeling coupled to electron paramagnetic resonance spectroscopy (SDSL-EPR) has emerged as a powerful tool for studying the structural properties of biomolecules directly in cells. Since the first in-cell EPR experiment was reported in 2010, substantial progress has been made, and this Review provides a detailed overview of the developments and applications of this spectroscopic technique. The strategies available for preparing a cellular sample and the EPR methods that can be applied to cells will be discussed. The array of spin labels available, along with their strengths and weaknesses in cellular contexts, will also be described. Several examples will illustrate how in-cell EPR can be applied to different biological systems and how the cellular environment affects the structural and dynamic properties of different proteins. Lastly, the Review will focus on the future developments expected to expand the capabilities of this promising technique.

Exploring the dynamics and structure of PpiB in living Escherichia coli cells using electron paramagnetic resonance spectroscopy

The combined effects of the cellular environment on proteins led to the definition of a fifth level of protein structural organization termed quinary structure. To explore the implication of potential quinary structure for globular proteins, we studied the dynamics and conformations of Escherichia coli (E. coli) peptidyl-prolyl cis/trans isomerase B (PpiB) in E. coli cells. PpiB plays a major role in maturation and regulation of folded proteins by catalyzing the cis/trans isomerization of the proline imidic peptide bond. We applied electron paramagnetic resonance (EPR) techniques, utilizing both Gadolinium (Gd(III)) and nitroxide spin labels. In addition to using standard spin labeling approaches with genetically engineered cysteines, we incorporated an unnatural amino acid to achieve Gd(III)-nitroxide orthogonal labeling. We probed PpiB's residue-specific dynamics by X-band continuous wave EPR at ambient temperatures and its structure by double electron-electron resonance (DEER) on frozen samples. PpiB was delivered to E. coli cells by electroporation. We report a significant decrease in the dynamics induced by the cellular environment for two chosen labeling positions. These changes could not be reproduced by adding crowding agents and cell extracts. Concomitantly, we report a broadening of the distance distribution in E. coli, determined by Gd(III)-Gd(III) DEER measurements, as compared with solution and human HeLa cells. This suggests an increase in the number of PpiB conformations present in E. coli cells, possibly due to interactions with other cell components, which also contributes to the reduction in mobility and suggests the presence of a quinary structure.

Modeling of Cu(II)-based protein spin labels using rotamer libraries

The bifunctional spin label double-histidine copper-(II) capped with nitrilotriacetate [dHis-Cu(II)-NTA], used in conjunction with electron paramagnetic resonance (EPR) methods can provide high-resolution distance data for investigating protein structure and backbone conformational diversity. Quantitative utilization of this data is limited due to a lack of rapid and accurate dHis-Cu(II)-NTA modeling methods that can be used to translate experimental data into modeling restraints. Here, we develop two dHis-Cu(II)-NTA rotamer libraries using a set of recently published molecular dynamics simulations and a semi-empirical meta-dynamics-based conformational ensemble sampling tool for use with the recently developed chiLife bifunctional spin label modeling method. The accuracy of both the libraries and the modeling method are tested by comparing model predictions to experimentally determined distance distributions. We show that this method is accurate with absolute deviation between the predicted and experimental modes between 0.0-1.2 Å with an average of 0.6 Å over the test data used. In doing so, we also validate the generality of the chiLife bifunctional label modeling method. Taken together, the increased structural resolution and modeling accuracy of dHis-Cu(II)-NTA over other spin labels promise improvements in the accuracy and resolution of protein models by EPR.

Pulse Dipolar Electron Paramagnetic Resonance Spectroscopy Distance Measurements at Low Nanomolar Concentrations: The CuII-Trityl Case

Recent sensitivity enhancements in pulse dipolar electron paramagnetic resonance spectroscopy (PDS) have afforded distance measurements at submicromolar spin concentrations. This development opens the path for new science as more biomolecular systems can be investigated at their respective physiological concentrations. Here, we demonstrate that the combination of orthogonal spin-labeling using CuII ions and trityl yields a >3-fold increase in sensitivity compared to that of the established CuII-nitroxide labeling strategy. Application of the recently developed variable-time relaxation-induced dipolar modulation enhancement (RIDME) method yields a further ∼2.5-fold increase compared to the commonly used constant-time RIDME. This overall increase in sensitivity of almost an order of magnitude makes distance measurements in the range of 3 nm with protein concentrations as low as 10 nM feasible, >2 times lower than the previously reported concentration. We expect that experiments at single-digit nanomolar concentrations are imminent, which have the potential to transform biological PDS applications.

Integrating Electron Paramagnetic Resonance Spectroscopy and Computational Modeling to Measure Protein Structure and Dynamics

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large-amplitude conformational transition, a coarse-grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE-MD including all intermediate states.

PDSFit: PDS data analysis in the presence of orientation selectivity, g-anisotropy, and exchange coupling

Pulsed dipolar electron paramagnetic resonance spectroscopy (PDS), encompassing techniques such as pulsed electron-electron double resonance (PELDOR or DEER) and relaxation-induced dipolar modulation enhancement (RIDME), is a valuable method in structural biology and materials science for obtaining nanometer-scale distance distributions between electron spin centers. An important aspect of PDS is the extraction of distance distributions from the measured time traces. Most software used for this PDS data analysis relies on simplifying assumptions, such as assuming isotropic g-factors of ~2 and neglecting orientation selectivity and exchange coupling. Here, the program PDSFit is introduced, which enables the analysis of PELDOR and RIDME time traces with or without orientation selectivity. It can be applied to spin systems consisting of up to two spin centers with anisotropic g-factors and to spin systems with exchange coupling. It employs a model-based fitting of the time traces using parametrized distance and angular distributions, and parametrized PDS background functions. The fitting procedure is followed by an error analysis for the optimized parameters of the distributions and backgrounds. Using five different experimental data sets published previously, the performance of PDSFit is tested and found to provide reliable solutions.