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Herein, we develop an efficient rotamer library-based approach to predict distance distributions from Cu(ii) protein labels.

 

Combining rigid Cu( ii ) labels and pulsed-EPR techniques enables distance constraint measurements that are incisive probes of protein structure and dynamics. However, the labels can lead to a dipolar signal that is biased by the relative orientation of the two spins, which is typically unknown a priori in a bilabeled protein. This effect, dubbed orientational selectivity, becomes a bottleneck in measuring distances. This phenomenon also applies to other pulsed-EPR techniques that probe electron–nucleus interactions. In this work, we dissect orientational selectivity by generating an in silico sample of Cu( ii )-labeled proteins to evaluate pulse excitation in the context of double electron–electron resonance (DEER) at Q-band frequencies. This approach enables the observation of the contribution of each protein orientation to the dipolar signal, which provides direct insights into optimizing acquisition schemes to mitigate orientational effects. Furthermore, we incorporate the excitation profile of realistic pulses to identify the excited spins. With this method, we show that rectangular pulses, despite their imperfect inversion capability, can sample similar spin orientations as other sophisticated pulses with the same bandwidth. Additionally, we reveal that the efficiency of exciting spin-pairs in DEER depends on the frequency offset of two pulses used in the experiment and the relative orientation of the two spins. Therefore, we systematically examine the frequency offset of the two pulses used in this double resonance experiment to determine the optimal frequency offset for optimal distance measurements. This procedure leads to a protocol where two measurements are sufficient to acquire orientational-independent DEER at Q-band. Notably, this procedure is feasible with any commercial pulsed-EPR spectrometer. Furthermore, we experimentally validate the computational results using DEER experiments on two different proteins. Finally, we show that increasing the amplitude of the rectangular pulse can increase the efficiency of DEER experiments by almost threefold. Overall, this work provides an attractive new approach for analyzing pulsed-EPR spectroscopy to obtain microscopic nuances that cannot be easily discerned from analytical or numerical calculations. 

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.

 

The catalytic activity of human glutathione S‐transferase A1‐1 (hGSTA1‐1), a homodimeric detoxification enzyme, is dependent on the conformational dynamics of a key C‐terminal helix α9 in each monomer. However, the structural details of how the two monomers interact upon binding of substrates is not well understood and the structure of the ligand‐free state of the hGSTA1‐1 homodimer has not been resolved. Here, we used a combination of electron paramagnetic resonance (EPR) distance measurements and weighted ensemble (WE) simulations to characterize the conformational ensemble of the ligand‐free state at the atomic level. EPR measurements reveal a broad distance distribution between a pair of Cu(II) labels in the ligand‐free state that gradually shifts and narrows as a function of increasing ligand concentration. These shifts suggest changes in the relative positioning of the two α9 helices upon ligand binding. WE simulations generated unbiased pathways for the seconds‐timescale transition between alternate states of the enzyme, leading to the generation of atomically detailed structures of the ligand‐free state. Notably, the simulations provide direct observations of negative cooperativity between the monomers of hGSTA1‐1, which involve the mutually exclusive docking of α9 in each monomer as a lid over the active site. We identify key interactions between residues that lead to this negative cooperativity. Negative cooperativity may be essential for interaction of hGSTA1‐1 with a wide variety of toxic substrates and their subsequent neutralization. More broadly, this work demonstrates the power of integrating EPR distances with WE rare‐events sampling strategy to gain mechanistic information on protein function at the atomic level.

 

Recent advances in site-directed Cu 2+ labeling of proteins and nucleic acids have added an attractive new methodology to measure the structure-function relationship in biomolecules. Despite the promise, accessing the higher sensitivity of Q-band Double Electron Electron Resonance (DEER) has been challenging for Cu 2+ labels designed for proteins. Q-band DEER experiments on this label typically require many measurements at different magnetic fields, since the pulses can excite only a few orientations at a given magnetic field. Herein, we analyze such orientational effects through simulations and show that three DEER measurements, at strategically selected magnetic fields, are generally sufficient to acquire an orientational-averaged DEER time trace for this spin label at Q-band. The modeling results are experimentally verified on Cu 2+ labeled human glutathione S-transferase (hGSTA1-1). The DEER distance distribution measured at the Q-band shows good agreement with the distance distribution sampled by molecular dynamics (MD) simulations and X-band experiments. The concordance of MD sampled distances and experimentally measured distances adds growing evidence that MD simulations can accurately predict distances for the Cu 2+ labels, which remains a key bottleneck for the commonly used nitroxide label. In all, this minimal collection scheme reduces data collection time by as much as six-fold and is generally applicable to many octahedrally coordinated Cu 2+ systems. Furthermore, the concepts presented here may be applied to other metals and pulsed EPR experiments.