Paramagnetic solid-state NMR of proteins

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

Contents lists available at ScienceDirect

Solid State Nuclear Magnetic Resonance journal homepage: www.elsevier.com/locate/ssnmr

Paramagnetic solid-state NMR of proteins Ming Tang *, Dennis Lam Department of Chemistry, College of Staten Island
Ph.D. Programs in Chemistry and Biochemistry, The Graduate Center of the City University of New York, New York, NY, 10016, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Paramagnetic relaxation effect Pseudo-contact shift Solid-state NMR Microcrystalline proteins Protein complexes Protein aggregates Amyloid fibrils Membrane proteins

The paramagnetic properties of metal ions and stable radicals can affect NMR spectra, which can lead to changes in peak intensities, relaxation times and chemical shifts. The changes from paramagnetic effects provide intriguing opportunities for solid-state NMR studies of proteins. In this review, we summarized the trends and progress of paramagnetic solid-state NMR of proteins in the past decade, and showed that paramagnetic effects have great potential applications for sensitivity enhancement, structure determination and topological analysis for microcrystalline proteins, protein complexes, protein aggregates and membrane proteins.

1. Introduction A fascinating phenomenon, the magnetic properties of metal ions and stable radicals (spin labels) can influence NMR signals. For example, Cu2þ greatly reduces T1 relaxation times affecting sampling times; stable radical, Mn2þ and Gd3þ greatly reduce T2 relaxation times affecting signal intensities; Co2þ, Ni2þ, Ce3þ, Yb3þ, Dy3þ change the chemical shifts of nearby nuclei by various degrees. As a result, the chemical shift perturbation from the pseudo-contact shift (PCS), the signal attenuation and the reduction of T1 relaxation times from the paramagnetic relaxation enhancement (PRE) are strongly correlated with the distances between protein residues and spin labels or metal ions (5–40 Å). This correlation has been well characterized and used for determining structures of soluble proteins by solution NMR [1–3]. In addition, solvent PREs have been used to probe the protein-solvent interactions. In the past 20 years, solid-state NMR (SSNMR) techniques have been advanced significantly for proteins to provide structural insights for microcrystalline proteins, protein complexes, amyloidogenic and membrane proteins [4–8]. The emerging advances in instrumentation and methodology, such as multidimensional correlation experiments, 1H-detection, fast magic angle spinning (MAS), dynamic nuclear polarization (DNP) and so on, enable the applications of paramagnetic effects for solid proteins. Resonance assignments, structural restraints and solvent accessibility can be obtained by paramagnetic SSNMR method, similar to the applications in solution NMR, without the requirement of fast tumbling of the protein molecules. Microcrystalline protein samples can achieve similar

sensitivity as solution NMR, and thus they are readily used in methodology development. The size limitation of large protein complexes can be overcome by paramagnetic methods developed from microcrystalline proteins. Protein aggregates and membrane proteins are difficult targets for solution NMR due to their insoluble nature, while SSNMR can provide optimal sensitivity and resolution for implementation and observation of paramagnetic effects for these insoluble proteins. Therefore, this review is divided into three parts to discuss the advancement of paramagnetic SSNMR techniques in the past decade: 1) Methodology development; 2) Applications in protein aggregates; 3) Applications in membrane proteins. 2. Methodology development The development of paramagnetic methods in SSNMR is primarily based on the concept of utilizing changes induced by paramagnetic effects to accelerate data acquisition, distance measurement, topological analysis (determination of orientation and interface). Due to the intrinsic nature of low sensitivity in SSNMR compared to other structural tools, acceleration is a key feature and appealing factor in the development of paramagnetic techniques to overcome the challenges in SSNMR. Hence, the general theme of these methods is to utilize a combination of paramagnetic tags, sample conditions, instrument specialization, pulse sequence modification and computational analysis to speed up the process of acquiring relevant data (assignments, distances or topology) in a given amount of time.

* Corresponding author. E-mail address: [email protected] (M. Tang). https://doi.org/10.1016/j.ssnmr.2019.101621 Received 5 June 2019; Received in revised form 24 September 2019; Accepted 24 September 2019 Available online xxxx 0926-2040/Published by Elsevier Inc.

Please cite this article as: M. Tang, D. Lam, Paramagnetic solid-state NMR of proteins, Solid State Nuclear Magnetic Resonance, https://doi.org/ 10.1016/j.ssnmr.2019.101621

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

2.1. Sensitivity enhancement

2.2. Distance measurement

The basic idea behind paramagnetic methods to enhance SSNMR signals is to decrease the recycle delay between scans in order to acquire more data in the same amount of time as traditional methods. The recycle delay is mainly limited by 1H T1 relaxation time, which can be shortened by 5–10 times if paramagnetic tags like Cu2þ were incorporated in samples. However, with small recycle delays ~0.2 s or less, the sample heating and high power decoupling become problematic and cause burdens on both samples and instruments. Because of that, special probes with fast magic-angle spinning (spin rate > 20 kHz) or perdeuterated samples have been used to relieve the need for high power decoupling, and thus data acquisition becomes rapid without worrying about sample heating or probe arcing. These implementations are demonstrated for the microcrystalline lysozyme, ubiquitin and amyloid fibrils with Cu-EDTA [9,10], the perdeuterated SH3 domain of chicken α-spectrin with Cu-EDTA [11,12], the perdeuterated microcrystalline dimeric human superoxide dismutase (SOD) with Cu2þ in one metal center [13,14], the microcrystalline cobalt(II)-substituted catalytic domain of matrix metalloproteinase 12 (CoMMP-12) [15], the dynein light chain 8 (LC8) with Cu-EDTA [16,17], the protein complex of the receptor cytoplasmic fragment, a kinase, a coupling protein with Ni2þ bound to lipid [18], and the microcrystalline B1 Ig binding domain of protein G (GB1) with Cu-EDTA [19,20] or sidechain modified Cu-EDTA complex [21]. In most cases of these studies, protein microcrystals are surrounded by solutions with Cu-EDTA (concentration 10–150 mM), which reduces 1H T1 relaxation times of water and protons on the crystal surface, and then 1H T1 relaxation times of proteins inside crystals are reduced through proton spin diffusion. Because Cu2þ ions are bound to EDTA, no interactions between Cu2þ and proteins would occur. The 1H T1 PRE effects are mediated through spin diffusion, and thus direct T2 PRE effects of Cu2þ on nearby proteins are minimized. Special conditions of samples like metalloproteins or protein complexes need to be considered when incorporating paramagnetic tags. For metalloproteins, metal centers can be used to add paramagnetic metal ions like SOD with Cu2þ in one metal center [13], in which only a handful of residues within 8 Å of the metal center are affected by T2 PRE effects as a good trade-off for much faster data acquisition. The influences of the metal center on the nearby residues can be reduced through ultra-fast MAS in the case of CoMMP-12, where 60 kHz MAS allows the detections of residues coordinating the metal center under 21.2 T magnetic field [15]. For protein complexes like bacterial chemotaxis receptor [18], lipids are used to bind Ni2þ ions to limit interactions between paramagnetic tags and proteins, and spin diffusion helps to spread 1H T1 PRE effects. In all the cases of sensitivity enhancement, fast magic-angle spinning (spin rate 40–100 kHz) is required to enable rapid pulsing with low power decoupling. Therefore, traditional pulse sequences that are used at low spin rate have to be modified to take in account of high spin rate [9,12,16,18] or new pulse sequences are developed specifically for rapid data acquisition [11,13, 17,19]. Further, perdeuteration of protein samples is complimentary with fast MAS and PRE effects, which enables 1H detection to push the limit of sensitivity to be close to solution NMR [11–13]. 1H detection can even be achieved for non-deuterated protein samples through ultra-fast MAS (spin rate 80–100 kHz) to significantly reduce 1H–1H couplings [19]. Finally, spectral editing techniques can be utilized to greatly reduce data acquisition time of multidimensional experiments. Non-uniform sampling (NUS) requires good sensitivity to make reasonable predictions, which is complimented by sensitivity enhancement from PRE effects [16,19]. Overall, microcrystalline proteins serve as good model systems to test incorporation of paramagnetic tags, pulse sequences and spectral analysis. Ultimately, PRE sensitivity enhancement techniques would be applied to other protein systems like protein complexes, protein aggregates and membrane proteins that really need the boost of signals, which are discussed in the later sections.

Besides sensitivity enhancement, another aspect of paramagnetic effects is commonly used in solution NMR to get structural information based on the fact that paramagnetic effects are typically proportional to 1/rn (n ¼ 6 for PRE and 3 for PCS), where r is the distance between the paramagnetic tags and nuclei. In practice, paramagnetic effects are not easy to quantify precisely, and thus a broad range of distances are estimated based on experimental data. Nonetheless, structural information can be very useful because of the sheer number of long-range distance restraints collected through paramagnetic effects (Fig. 1). In recent years, distance measurements comparable to solution NMR have been achieved in SSNMR to provide insights into protein folding and dynamics through PRE or PCS effects (Fig. 2). PRE effects are typically employed through monitoring T1 or T2 relaxation enhancement of detecting nuclei (mostly 13C, 15N) by paramagnetic tags (spin label or transition metal ions). Distance information is extrapolated from reductions of T1 times (increased R1 rates) or signal intensities (increased R2 rates). Distances measurements from T1 PRE effects are demonstrated for a microcrystalline GB1 with site mutations and modified Cu-EDTA side chains [22–25], a microcrystalline perdeuterated SOD with Cu2þ at one metal center (Fig. 2b) [26], and the Y145Stop human prion protein fibrils (huPrP23-144) modified with Cu-EDTA tags [27]. Distance restraints from T2 PRE effects are collected for microcrystalline GB1 with Mn-EDTA tags [28], and huPrP23-144 fibrils with spin labels [27]. The typical distance restraints range from 10 to 20 Å, which define the upper limit of distances between paramagnetic tags and observing sites. Structure calculations are mostly performed in Xplor-NIH [29]. However, due to the difficulties of quantifying PRE effects, additional modeling methods are used in some cases to reduce ambiguity, such as protein fragment search with sparse NMR data (CS-Rosetta) [28,30,31] and Modeling Employing Limited Data (MELD) [24,32]. These modeling methods help to yield consistent structures when PRE distance restraints alone are not sufficient to refine structures with conventional methods. The main challenges of PRE methods to extract distances are incorporation of paramagnetic tags and data analysis. Paramagnetic tags have to be attached to proteins through mutations of certain residues with bulky sidechains, where no disruption of structural integrity has to be confirmed, unless the target protein has native metal binding sites. The uncertainty of PRE data due to spectral overlap and ambiguous assignments has to be addressed with acquiring a large number of datasets with multiple samples with different tag locations. Despite of these challenges, recent advances in computational modeling methods can really improve the quality of structures generated from PRE data [24,28]. PCS effects from certain transition metal ions (Co2þ and some lanthanide ions) can also be measured to provide an estimated distance from metal center to specific residues that are influenced as chemical shift changes. The changes correlate with the distances (1/r3) and the parameters of the paramagnetic tensor (anisotropy and orientation) in the protein frame. The spectral analysis could be complicated because additional assignments might be needed to discern where the original peaks have moved to when paramagnetic tags are present. Some successes in PCS based structure determination have been found in the cases of a microcrystalline cobalt(II)-substituted metalloprotein CoMMP-12 (Fig. 2a) [33–35], a perdeuterated Co2þ-substituted superoxide dismutase (Co2þ- SOD) [36], and microcrystalline GB1 samples with site modified 4-mercaptomethyl-dipicolinic acid (4MMDPA) side chains and Co2þ, Yb3þ and Tm3þ ions [37]. The interpretation of PCS data requires additional analysis of tensor parameters estimated based on known structures or optimization through simulations, which makes the process of structure calculations longer and more complex compared to calculations with conventional distance data (13C–13C and 15N–13C distances). 13 13 C– C and 15N–13C short-range distances from conventional SSNMR experiments help to define local secondary structures, whereas PCS data are better suited for restraining tertiary structures [33,34,36]. When 2

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

Fig. 1. Schematic diagram to show how distance information is extracted from PRE and PCS data. (a) A protein model with 4 helices and a paramagnetic tag. Dashed arrows indicate the increasing distances between the tag and 4 representative residues (1–4) on the four helices. (b) Virtual 2D spectral comparison of the protein without (left) and with (right) a paramagnetic tag (e.g. Mn2þ for PRE). Stronger reductions of peak intensities indicate shorter distances between the tag and the residues. (c) An overlay of virtual 2D spectra of the protein without (black) and with (color) a paramagnetic tag (e.g. Co2þ for PCS). Larger peak shifts indicate shorter distances between the tag and the residues.

Fig. 2. Representative structures of proteins solved or validated by paramagnetic SSNMR. a) Structure of a cobalt(II)-substituted catalytic domain of matrix metalloproteinase 12 (CoMMP-12) (PDB ID: 2KRJ). b) Structure of a Cu2þ-substituted superoxide dismutase (Cu2þ- SOD) (PDB ID: 2LU5). c) Structure of a trimeric Anabaena Sensory Rhodopsin (ASR) (PDB ID: 2M3G). d) Structure of the N-terminal caspase recruitment domain of mitochondrial antiviral signaling protein (MAVS CARD) filaments (PDB ID: 2MS7). All the images are generated from the 3D viewer on PDB website (www.rcsb.org).

additional need of optimizations of tensor parameters during structural calculation. However, PCS effects have an important advantage that the range of distances is longer due to lesser distance dependence (1/r3) than PRE effects (1/r6), and thus intermolecular distance restraints can be

short-range distances are lacking, PCS data can be used in protein fragment search program (Rosetta) to calculate reasonably folded structures [37]. Compared to PRE data, PCS data have the similar challenges in the incorporation of paramagnetic tags and spectral analysis with the 3

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

dopants in buffer solutions has fewer concerns with disrupting protein structure. There are still some challenges remaining for applications of paramagnetic effects in complex systems. Parallel comparison of spectra for proteins with and without paramagnetic tags is essential to observe the changes caused by the tags, which creates additional workload for sample preparation and data analysis. Streamlining the spectral comparison for extracting PRE and PCS restraints would be the next significant step to facilitate the process. For example, the algorithms for graphical analysis can be utilized to simplify the spectra for comparison. A technique has been used to identify structural models that numerically fit the unassigned NMR spectra through scoring chemical shift predictions [55]. Similar algorithms can be developed to isolate the residues affected by paramagnetic effects.

obtained from PCS data to reveal information about crystal packing in microcrystalline proteins [35]. Therefore, PCS effects have a great potential application in providing information about interfaces between domains in protein complexes. 2.3. Other applications As for protein complexes, paramagnetic effects have been used in various cases depending on how paramagnetic tags are incorporated. For examples, surface accessibility of protein crystals like the perdeuterated chicken α-spectrin SH3 domain can be probed by PRE effects of Cu-EDTA in solutions surrounding the crystals [38]; Cu-EDTA is introduced to the bone tissues to probe the relative proximity of the collagen to the bone mineral surface with PRE effects [39]; Cu-EDTA is used to enhance the signals of huge sedimented protein complexes (The 20S proteasome with isotopically labeled α-subunit) [40]; TmDOTP is employed to isolate signals from the internal water of Pf1 bacteriophage filaments from the external water which intensities are reduced by PRE effects [41]; Gd-DTPA is utilized to validate the structure of the N-terminal caspase recruitment domain of mitochondrial antiviral signaling protein (MAVS CARD) filaments through PRE analysis for solvent accessibility (Fig. 2d) [42]; Gd(DTPA-BMA) is used to characterize protein-protein interfaces between GB1 and immunoglobulin G (IgG) complex through 1H and 15N PREs under fast MAS [43]. These studies carry a general theme of using paramagnetic dopants to probe interfaces between protein and solvent or protein and protein by observing PREs on specific residues. If there are PREs observed on the residues, those residues would be solvent accessible. If there are differences of PREs on the residues between free protein and bound protein, those residues would be at the protein-protein interface. Recently, DNP technology has been advanced significantly to provide high sensitivity enhancement for challenging systems at the cryogenic temperatures [44,45]. Paramagnetic radicals are necessary to transfer magnetization from electrons to nuclei, and thus they can also provide paramagnetic enhancement to the protein samples, such as the Acinetobacter phage 205 (AP205) nucleocapsid [46], membrane peptides in phospholipids [47], E. coli dihydrofolate reductase with a biradical-derivatized trimethoprim ligand [48], the galactophilic lectin LecA with a functionalized ligand (D-galactose linked to the bis-nitroxide TOTAPOL) [49], CsgA amyloid fibrils with TOTAPOL [50], and the membrane protein KcsA with site-mutations and covalently attached radicals [51,52]. Despite the large line broadening at low temperatures, the DNP-enhanced spectra of these protein samples indeed yield some structural information about the local residues near the radicals. In addition to paramagnetic dopants from outside sources, native paramagnetic metal centers can be quite useful for providing information about the metal ion binding of β-Sheet Nanocrystals within Caddisfly Silk by PRE effects [53] and the iron-sulfur cluster in the oxidized high-potential iron-sulfur protein I from Ectothiorhodospira halophila (EhHiPIP I) under fast MAS by PCS effects [54]. The versatility and flexibility of paramagnetic methods benefit from the large pool of paramagnetic tags to choose and the ways of incorporation to access the sites of interest. With the complimentary techniques like fast MAS, perdeuteration and computational modelling, paramagnetic methods can really enhance the applications of SSNMR in solid proteins. Fig. 3 illustrates the general procedures to incorporate paramagnetic tags into the protein system in order to gain sensitivity or extract structural information about the target proteins. It should be noted that the structural or functional integrity must be confirmed for the proteins with the methods of metal center replacement or Cys mutation. As for the situation where there are more than one accessible Cys site, it would not affect the applications for sensitivity enhancement, but it would make the interpretation of distance restraints more complicated. So it is important to change other Cys sites to non-Cys residues and leave only the desired Cys site attached with the paramagnetic tag to make the distances between the tag and the residues unambiguous. As a comparison, adding paramagnetic

3. Applications in protein aggregates Diseases related to protein misfolding, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, prion diseases, and type II diabetes, are associated with insoluble amyloid fibril formation. These amyloid-forming proteins are produced as soluble proteins, progress into oligomeric forms under certain conditions, and finally aggregate into the insoluble fibrillar deposits. Solving the supramolecular structures of protein aggregates or fibrils would provide insights into the mechanism of toxicity of fibrillar proteins and form a basis for improving diagnostic tools and therapies for those diseases. These aggregates are typically insoluble and lacking long-range order other than along the propagation axis, and thus they are difficult to study with conventional structural tools like X-ray crystallography and solution NMR. SSNMR is a versatile and powerful tool specialized in the insoluble systems with no long-range order, and several structures of protein aggregates and amyloid fibrils have been solved by SSNMR [7,8,56–60]. However, the challenges of sample sensitivity, resolution and spectral assignment still remain for protein aggregates due to their nature of repetitive primary sequence and polymorphism of fibrils. Hence, the development of paramagnetic methods has been contributing significantly to improving SSNMR analysis of protein aggregates in sensitivity enhancement and structure determination. Sensitivity enhancement of protein aggregates by paramagnetic effects uses the same concept as that of microcrystalline proteins shown in the previous section. The benefits are two-fold: 1) Higher sensitivity enables the collection of large multidimensional datasets to overcome spectral overlap for protein aggregates; 2) Different labeling strategies can be explored with less restriction on sample quantities with enhanced sensitivity in order to target specific regions of aggregates involved in the mechanism of self-assembly. Enhancement of 1H T1 relaxation times is demonstrated for Aβ14-23 peptide fibrils mixed with glycerol/water and Dy-EDTA at 25 K [61], and significant reduction of experimental times (around 5 fold) has been achieved for Aβ1-40 and Aβ1-42 peptide fibrils with Cu-EDTA under fast MAS [9]. The sample quantities of amyloid fibrils in those studies are less than 3 mg, which makes it possible to explore different sample conditions and labeling strategies for similar protein aggregates. Structural aspects of protein aggregates can be probed by introducing paramagnetic tags in different ways to observe changes from PRE effects and derive information about metal-binding sites, interfaces and molecular packing. For instances, Cuþ/Cu2þ-binding structures of Aβ1-40 peptide fibrils is elucidated by detecting PRE T2 effects of bound Cuþ/ Cu2þ ions on specific residues for potential explanation of the production of neuro toxic reactive oxygen species [62,63]; Fibril-solvent interfaces of Y145Stop human prion protein fibrils (huPrP23-144) are investigated by 15 N PRE effects from Cu-EDTA solution [64]; Protein fold and protofilament assembly of huPrP23-144 are determined by the measurements of PREs from spin-label or Cu-EDTA tags [27]. These studies have shown the versatility of incorporation of paramagnetic tags to yield rich structural information about protein aggregates, especially the long-range intermolecular distances that are inaccessible by conventional SSNMR. The advantage of acquiring long-range distance restraints by 4

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

Fig. 3. Flowchart to show the general ways of incorporating paramagnetic tags for different purposes of the studies of target proteins (sensitivity enhancement or distance measurement).

Rational design strategies have matured for soluble protein targets, and there is hope that such approaches can be applied to membrane proteins. However, their structures are often difficult to characterize in a membranous environment, due to their hydrophobic nature and lack of longrange order. SSNMR proves to be a resourceful approach to address many structural and biophysical questions in membrane proteins [4,5,68]. Recent advances in sample preparation and instrumentation have enabled the applications of paramagnetic effects on membrane proteins. The strategies of incorporation of paramagnetic tags would be different for membrane proteins due to the presence of lipids, which actually opens up new opportunities of developing tags with amphipathic properties. The usage of traditional paramagnetic tags such as metal-bound complexes or spin labels has been demonstrated for a variety of membrane proteins for the purpose of sensitivity enhancement. For examples, Cu-EDTA is used to reduce 1H T1 relaxation times for an Anabaena Sensory Rhodopsin (ASR) and a perdeuterated proteorhodopsin (PR) to provide good sensitivity under fast MAS [69]; Gd-DOTA is found to be effective to improve signal-to-noise ratio per unit time for a 7TM light-driven proton pump - green proteorhodopsin (PR) with much lower dopant concentration [70]; 5-DOXYL stearic acid radical is used to increase sensitivity by 3 fold for a Pf1 coat protein in the magnetically

paramagnetic tags is quite clear for protein aggregates, because the repetitive nature of primary sequences of aggregates makes it extremely difficult to distinguish intramolecular and intermolecular correlations between the same or similar residues, whereas the data of the aggregates formed by labeled proteins and natural abundance proteins with paramagnetic tags can provide unambiguous assignments of intermolecular contacts between different protein monomers. It should be noted that the recent development of cryo-EM technology has made it possible to analyze large fibril filaments with reasonable resolutions to provide structural information about the assembly of amyloid fibrils [65–67]. The information obtained from cryo-EM and SSNMR can be complimentary to each other. For example, cryo-EM data can provide the general fold of the fibrils, and SSNMR data can provide the local structures of the intermediate oligomers before the fibril formation. Overall, the general process of incorporating paramagnetic tags in protein aggregates is similar to the one shown in Fig. 3, except the part in which the integrity of aggregates or fibrils needs checking to confirm no disruption from metal center replacement or Cys mutation. 4. Applications in membrane proteins Membrane proteins represent more than half of current drug targets. 5

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx

of nicotinic acetylcholine receptor (M2δ nAChR) are used to affect 31P SSNMR of lipid headgroups [82]. The 31P PRE data from series of M2δ mutants suggest an insertion model of M2δ peptide consistent with the literature results. In general, the special feature of the amphipathic lipid bilayer structure provides unique opportunities to incorporate paramagnetic tags, whether in the buffer solutions or in the lipids (apart from the ways shown in Fig. 3), so as to extract structural or topological information about membrane proteins and ligands. The main challenge of using paramagnetic tags in membrane proteins is acquiring enough labeled proteins, since at least two samples (diamagnetic and paramagnetic) are needed for spectral comparison and the required quantities of labeled proteins are higher. Two potential ways of overcoming the difficulties of low yields of labeled membrane proteins can be explored: one is developing exchangeable tags that can be replaced in situ to reduce the required quantities of labeled proteins (i.e. designing molecules with paramagnetic tags that can be exchanged in liposomes through chemical means or dialysis); the other is using computational modeling of protein topology in the membrane to predict spectral changes that numerically fit SSNMR data or to use machine learning algorithms to facilitate the assignments of PRE and PCS restraints. Hence, the need for comprehensive multidimensional data collection and the large amount of labeled proteins can be reduced.

aligned bicelles [71]. Intriguingly, the strategy of covalently attaching EDTA tag to the proteins is less effective compared to complex dopants in buffer solution, where ASR with one cysteine mutation site only gives 1.5 fold reduction in 1H T1 [69] and PR with three cysteine sites and 15 fold molar excess Cu2þ gives 6 fold reduction in 1H T1 [70]. This observation makes the way of covalently attaching paramagnetic tags to the proteins for sensitivity enhancement less appealing considering the extra steps of mutating specific sites and confirming the structural integrity. A novel headgroup modified chelator lipid (DMPE-DTPA) has been developed to attach the complex motif similar to EDTA to incorporate metal ions efficiently in the lipid vesicle systems with better compatibility [72]. This chelator lipid with Cu2þ has been shown to enhance SSNMR signals for a membrane peptide subtilosin A in bicelles [72], an integral membrane protein DsbB in proteoliposomes [73], a membrane protein cytochrome-b5 in lipid vesicles [74] and a small membrane protein sarcolipin in oriented bicelles [75]. The typical enhancement of 1 H T1 ranges from 3 to 10 fold, depending on sample conditions and the concentrations of Cu-DMPE-DTPA. The advantages of chelator lipids over traditional dopants are: 1) the concentrations of ions in the buffer are much lower since the paramagnetic tags are bound to lipids, and thus sample heating due to excess ion concentration is not an issue; 2) the sample integrity is not disturbed since the paramagnetic tags are not bound to the proteins. Aside from sensitivity enhancement, structural information can also be obtained through paramagnetic effects on membrane proteins. If the membrane proteins have metal-binding sites, which can be replaced by paramagnetic metal ions, paramagnetic effects can be observed on the proteins to provide information about the proximity of specific residues to the metal-binding sites. For example, the interaction of a membraneembedded protein the Na,K-ATPase (NKA) with a13C-labeled cardiotonic steroid inhibitor (ODA) is probed by observing signal attenuation of the labeled inhibitor from a nearby bound Mn2þ on NKA, revealing the orientation of ODA in NKA [76]. Another study of a helicase (HpDnaB) and an ABC transporter (BmrA) with nucleotide binding domains also utilized the replacement of the ATP hydrolysis cofactor Mg2þ with Mn2þ in order to identify the residues with 15 Å radius of Mn2þ unambiguously, which is an effective approach for nucleotide bound proteins with metal cofactors [77]. In the case of no available metal-binding sites, paramagnetic tags can be covalently attached to proteins or ligands to provide long-range distance information about ligand binding and oligomeric interface. A nitroxide spin label is attached covalently to the S26C mutation site of a trimeric Anabaena Sensory Rhodopsin (ASR, 81 kDa) to provide PRE restraints for interhelical packing and the interfaces between monomers, which yields a trimeric structure with 0.8 Å backbone RMSD in the helices (Fig. 2c) [78]. An unnatural amino acid 2-amino-3-(8-hydroxyquinolin-3-yl)propanoic acid dihydrochloride (HQA) with chelated Mn2þ is incorporated into a G protein coupled receptor CXCR1 to study its interaction with the labeled human chemokine interleukin-8 (IL-8) ligand [79]. The proximity of IL-8 to the mutation site on CXCR1 is confirmed by PRE data. Paramagnetic cholesterol analogues are used to investigate the interaction between the cholesterol and a translocator protein TSPO [80]. Two analogues of cholesterol with nitroxide attached to different locations on cholesterol help to identify specific residues on TSPO that interact with two sides of cholesterol through PRE data. In these studies, traditional distances (13C–13C and 15 N–13C distances) are either too short or too ambiguous due to spectral overlap to define tertiary structure, while long-range distances from paramagnetic effects are critical to provide information about the organization of the protein-ligand or protein-protein interfaces. In addition to long-range distance restraints, membrane topology is also important structural information that can be obtained from paramagnetic effects. For instances, Mn2þ ions, which are on the outer but not the inner leaflet of lipid bilayers, help to identify the asymmetric insertion of a cell-penetrating peptide, penetratin, based on the measurements of PREs on labeled penetration from outer leaflet Mn2þ [81]. Site-specific nitroxide spin-labels on the δ-subunit of the transmembrane segment M2

5. Conclusions A large variety of applications of paramagnetic SSNMR methods in obtaining structural and topological information have been demonstrated for distinct classes of proteins spanning from small peptides to large protein complexes, which proves that paramagnetic SSNMR is widely applicable for structure-function-relationship studies of proteins. Paramagnetic SSNMR is on the precipice of a high-throughput revolution with the increasing automation of difficult experimental techniques and data analysis, and other breakthroughs in instrumentation (dynamic nuclear polarization and cryogenic probes for solids). The main challenges remain in the incorporation of paramagnetic tags, data analysis and structure calculation. In combination with the advances in synthesis of new compounds for paramagnetic tags, data sampling and computational modeling, paramagnetic SSNMR will yield better de novo or refined structures for the challenging protein complexes, protein aggregates and membrane proteins, and provide valuable information about their mechanisms of action. Declaration of competing interestCOI The authors declare no competing financial interests. Acknowledgment The authors thank PSC-CUNY (Award# 67148-00 45 and 62178-00 50) and College of Staten Island startup fund for the research support. References [1] M. Allegrozzi, I. Bertini, M.B.L. Janik, Y.M. Lee, G.H. Lin, C. Luchinat, Lanthanideinduced pseudocontact shifts for solution structure refinements of macromolecules in shells up to 40 angstrom from the metal ion, J. Am. Chem. Soc. 122 (2000) 4154–4161. [2] T. Saio, M. Yokochi, H. Kumeta, F. Inagaki, PCS-based structure determination of protein-protein complexes, J. Biomol. NMR 46 (2010) 271–280, https://doi.org/ 10.1007/s10858-010-9401-4. [3] A.J. Pell, G. Pintacuda, C.P. Grey, Paramagnetic NMR in solution and the solid state, Prog. Nucl. Magn. Reson. Spectrosc. 111 (2019) 1–271, https://doi.org/10.1016/ j.pnmrs.2018.05.001. [4] V. Ladizhansky, Applications of solid-state NMR to membrane proteins, Biochim. Biophys. Acta Protein Proteonomics 1865 (2017) 1577–1586, https://doi.org/ 10.1016/j.bbapap.2017.07.004. [5] V.S. Mandala, J.K. Williams, M. Hong, Structure and dynamics of membrane proteins from solid-state NMR, Annu. Rev. Biophys. 47 (2018) 201–222, https:// doi.org/10.1146/annurev-biophys-070816-033712. 6

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx [29] C.D. Schwieters, J.J. Kuszewski, N. Tjandra, G.M. Clore, The Xplor-NIH NMR molecular structure determination package, J. Magn. Reson. 160 (2003) 65–73. [30] Y. Shen, O. Lange, F. Delaglio, P. Rossi, J.M. Aramini, G. Liu, A. Eletsky, Y. Wu, K.K. Singarapu, A. Lemak, A. Ignatchenko, C.H. Arrowsmith, T. Szyperski, G.T. Montelione, D. Baker, A. Bax, Consistent blind protein structure generation from NMR chemical shift data, Proc. Natl. Acad. Sci. U.S.A. 105 (2008) 4685–4690, https://doi.org/10.1073/pnas.0800256105. [31] A. Cavalli, X. Salvatella, C.M. Dobson, M. Vendruscolo, Protein structure determination from NMR chemical shifts, Proc. Natl. Acad. Sci. U.S.A. 104 (2007) 9615–9620, https://doi.org/10.1073/pnas.0610313104. [32] J.L. MacCallum, A. Perez, K.A. Dill, Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference, Proc. Natl. Acad. Sci. 112 (2015) 6985–6990, https://doi.org/10.1073/pnas.1506788112. [33] S. Balayssac, I. Bertini, A. Bhaumik, M. Lelli, C. Luchinat, Paramagnetic shifts in solid-state NMR of proteins to elicit structural information, Proc. Natl. Acad. Sci. U.S.A. 105 (2008) 17284–17289, https://doi.org/10.1073/pnas.0708460105. [34] I. Bertini, A. Bhaumik, G. De Paepe, R.G. Griffin, M. Lelli, J.R. Lewandowski, C. Luchinat, High-resolution solid-state NMR structure of a 17.6 kDa protein, J. Am. Chem. Soc. 132 (2010) 1032–1040, https://doi.org/10.1021/ja906426p. [35] C. Luchinat, G. Parigi, E. Ravera, M. Rinaldelli, Solid-state NMR crystallography through paramagnetic restraints, J. Am. Chem. Soc. 134 (2012) 5006–5009, https://doi.org/10.1021/ja210079n. [36] M.J. Knight, I.C. Felli, R. Pierattelli, I. Bertini, L. Emsley, T. Herrmann, G. Pintacuda, Rapid measurement of pseudocontact shifts in metalloproteins by proton-detected solid-state NMR spectroscopy, J. Am. Chem. Soc. 134 (2012) 14730–14733, https://doi.org/10.1021/ja306813j. [37] J. Li, K.B. Pilla, Q. Li, Z. Zhang, X. Su, T. Huber, J. Yang, Magic angle spinning NMR structure determination of proteins from pseudocontact shifts, J. Am. Chem. Soc. 135 (2013) 8294–8303, https://doi.org/10.1021/ja4021149. [38] R. Linser, U. Fink, B. Reif, Probing surface accessibility of proteins using paramagnetic relaxation in solid-state NMR spectroscopy, J. Am. Chem. Soc. 131 (2009) 13703–13708, https://doi.org/10.1021/ja903892j. [39] K.H. Mroue, N. MacKinnon, J. Xu, P. Zhu, E. McNerny, D.H. Kohn, M.D. Morris, A. Ramamoorthy, High-resolution structural insights into bone: a solid-state NMR relaxation study utilizing paramagnetic doping, J. Phys. Chem. B 116 (2012) 11656–11661, https://doi.org/10.1021/jp307935g. [40] A. Mainz, T.L. Religa, R. Sprangers, R. Linser, L.E. Kay, B. Reif, NMR spectroscopy of soluble protein complexes at one mega-dalton and beyond, Angew. Chem. Int. Ed. 52 (2013) 8746–8751, https://doi.org/10.1002/anie.201301215. [41] I.V. Sergeyev, S. Bahri, L.A. Day, A.E. McDermott, Pf1 bacteriophage hydration by magic angle spinning solid-state NMR, J. Chem. Phys. 141 (2014) 22D533, https:// doi.org/10.1063/1.4903230. [42] L. He, B. Bardiaux, M. Ahmed, J. Spehr, R. K€ onig, H. Lünsdorf, U. Rand, T. Lührs, C. Ritter, Structure determination of helical filaments by solid-state NMR spectroscopy, Proc. Natl. Acad. Sci. 113 (2016) E272–E281, https://doi.org/ 10.1073/pnas.1513119113. € [43] C. Oster, S. Kosol, C. Hartlmüller, J.M. Lamley, D. Iuga, A. Oss, M.L. Org, K. Vanatalu, A. Samoson, T. Madl, J.R. Lewandowski, Characterization of proteinprotein interfaces in large complexes by solid-state NMR solvent paramagnetic relaxation enhancements, J. Am. Chem. Soc. 139 (2017) 12165–12174, https:// doi.org/10.1021/jacs.7b03875. [44] Q.Z. Ni, E. Daviso, T. V Can, E. Markhasin, S.K. Jawla, T.M. Swager, R.J. Temkin, J. Herzfeld, R.G. Griffin, High frequency dynamic nuclear polarization, Acc. Chem. Res. 46 (2013) 1933–1941, https://doi.org/10.1021/ar300348n. [45] R. Rogawski, A.E. McDermott, New NMR tools for protein structure and function: spin tags for dynamic nuclear polarization solid state NMR, Arch. Biochem. Biophys. 628 (2017) 102–113, https://doi.org/10.1016/j.abb.2017.06.010. [46] K. Jaudzems, A. Bertarello, S.R. Chaudhari, A. Pica, D. Cala-De Paepe, E. BarbetMassin, A.J. Pell, I. Akopjana, S. Kotelovica, D. Gajan, O. Ouari, K. Tars, G. Pintacuda, A. Lesage, Dynamic nuclear polarization-enhanced biomolecular NMR spectroscopy at high magnetic field with fast magic-angle spinning, Angew. Chem. Int. Ed. 57 (2018) 7458–7462, https://doi.org/10.1002/anie.201801016. [47] S.Y. Liao, M. Lee, T. Wang, I.V. Sergeyev, M. Hong, Efficient DNP NMR of membrane proteins: sample preparation protocols, sensitivity, and radical location, J. Biomol. NMR 64 (2016) 223–237, https://doi.org/10.1007/s10858-016-0023-3. [48] I. Marin-Montesinos, D. Goyard, E. Gillon, O. Renaudet, A. Imberty, S. Hediger, G. De Pa€epe, Selective high-resolution DNP-enhanced NMR of biomolecular binding sites, Chem. Sci. 10 (2019) 3366–3374, https://doi.org/10.1039/C8SC05696J. [49] R. Rogawski, I.V. Sergeyev, Y. Zhang, T.H. Tran, Y. Li, L. Tong, A.E. McDermott, NMR signal quenching from bound biradical affinity reagents in DNP samples, J. Phys. Chem. B 121 (2017) 10770–10781, https://doi.org/10.1021/ acs.jpcb.7b08274. [50] M. Nagaraj, T.W. Franks, S. Saeidpour, T. Schubeis, H. Oschkinat, C. Ritter, B.J. vanRossum, Surface binding of TOTAPOL assists structural investigations of amyloid fibrils by dynamic nuclear polarization NMR spectroscopy, ChemBioChem 17 (2016) 1308–1311, https://doi.org/10.1002/cbic.201600185. [51] E.A.W. van der Cruijsen, E.J. Koers, C. Sauvee, R.E. Hulse, M. Weingarth, O. Ouari, E. Perozo, P. Tordo, M. Baldus, Biomolecular DNP-supported NMR spectroscopy using site-directed spin labeling, Chem. Eur. J. 21 (2015) 12971–12977, https:// doi.org/10.1002/chem.201501376. [52] E.J. Koers, E.A.W. van der Cruijsen, M. Rosay, M. Weingarth, A. Prokofyev, C. Sauvee, O. Ouari, J. van der Zwan, O. Pongs, P. Tordo, W.E. Maas, M. Baldus, NMR-based structural biology enhanced by dynamic nuclear polarization at high magnetic field, J. Biomol. NMR 60 (2014) 157–168, https://doi.org/10.1007/ s10858-014-9865-8.

[6] V.A. Higman, Solid-state MAS NMR resonance assignment methods for proteins, Prog. Nucl. Magn. Reson. Spectrosc. 106–107 (2018) 37–65, https://doi.org/ 10.1016/j.pnmrs.2018.04.002. [7] A. Loquet, N. El Mammeri, J. Stanek, M. Berbon, B. Bardiaux, G. Pintacuda, B. Habenstein, 3D structure determination of amyloid fibrils using solid-state NMR spectroscopy, Methods 138–139 (2018) 26–38, https://doi.org/10.1016/ j.ymeth.2018.03.014. [8] R. Linser, Solid-state NMR spectroscopic trends for supramolecular assemblies and protein aggregates, Solid State Nucl. Magn. Reson. 87 (2017) 45–53, https:// doi.org/10.1016/j.ssnmr.2017.08.003. [9] N.P. Wickramasinghe, S. Parthasarathy, C.R. Jones, C. Bhardwaj, F. Long, M. Kotecha, S. Mehboob, L.W.M. Fung, J. Past, A. Samoson, Y. Ishii, Nanomole-scale protein solid-state NMR by breaking intrinsic 1H T1 boundaries, Nat. Methods 6 (2009) 215–218, https://doi.org/10.1038/nmeth.1300. [10] N.P. Wickramasinghe, M. Kotecha, A. Samoson, J. Past, Y. Ishii, Sensitivity enhancement in 13C solid-state NMR of protein microcrystals by use of paramagnetic metal ions for optimizing 1H T1 relaxation, J. Magn. Reson. 184 (2007) 350–356, https://doi.org/10.1016/j.jmr.2006.10.012. [11] R. Linser, U. Fink, B. Reif, Proton-detected scalar coupling based assignment strategies in MAS solid-state NMR spectroscopy applied to perdeuterated proteins, J. Magn. Reson. 193 (2008) 89–93, https://doi.org/10.1016/j.jmr.2008.04.021. [12] R. Linser, V. Chevelkov, A. Diehl, B. Reif, Sensitivity enhancement using paramagnetic relaxation in MAS solid-state NMR of perdeuterated proteins, J. Magn. Reson. 189 (2007) 209–216, https://doi.org/10.1016/j.jmr.2007.09.007. [13] E. Barbet-Massin, A.J. Pell, M.J. Knight, A.L. Webber, I.C. Felli, R. Pierattelli, L. Emsley, A. Lesage, G. Pintacuda, 13C-detected through-bond correlation experiments for protein resonance assignment by ultra-fast MAS solid-state NMR, ChemPhysChem 14 (2013) 3131–3137, https://doi.org/10.1002/cphc.201201097. [14] S. Laage, J.R. Sachleben, S. Steuernagel, R. Pierattelli, G. Pintacuda, L. Emsley, Fast acquisition of multi-dimensional spectra in solid-state NMR enabled by ultra-fast MAS, J. Magn. Reson. 196 (2009) 133–141, https://doi.org/10.1016/ j.jmr.2008.10.019. [15] I. Bertini, L. Emsley, M. Lelli, C. Luchinat, J. Mao, G. Pintacuda, Ultrafast MAS solidstate NMR permits extensive 13C and 1H detection in paramagnetic metalloproteins, J. Am. Chem. Soc. 132 (2010) 5558–5559, https://doi.org/ 10.1021/ja100398q. [16] S. Sun, S. Yan, C. Guo, M. Li, J.C. Hoch, J.C. Williams, T. Polenova, A time-saving strategy for MAS NMR spectroscopy by combining nonuniform sampling and paramagnetic relaxation assisted condensed data collection, J. Phys. Chem. B 116 (2012) 13585–13596, https://doi.org/10.1021/jp3005794. [17] X. Lu, H. Zhang, M. Lu, A.J. Vega, G. Hou, T. Polenova, Improving dipolar recoupling for site-specific structural and dynamics studies in biosolids NMR: windowed RN-symmetry sequences, Phys. Chem. Chem. Phys. 18 (2016) 4035–4044, https://doi.org/10.1039/c5cp07818k. [18] M. Kashefi, L.K. Thompson, Signaling-related mobility changes in bacterial chemotaxis receptors revealed by solid-state NMR, J. Phys. Chem. B 121 (2017) 8693–8705, https://doi.org/10.1021/acs.jpcb.7b06475. [19] Y. Ishii, A. Wickramasinghe, I. Matsuda, Y. Endo, Y. Ishii, Y. Nishiyama, T. Nemoto, T. Kamihara, Progress in proton-detected solid-state NMR (SSNMR): super-fast 2D SSNMR collection for nano-mole-scale proteins, J. Magn. Reson. 286 (2018) 99–109, https://doi.org/10.1016/j.jmr.2017.11.011. € [20] J.M. Lamley, D. Iuga, C. Oster, H.-J. Sass, M. Rogowski, A. Oss, J. Past, A. Reinhold, S. Grzesiek, A. Samoson, J.R. Lewandowski, Solid-state NMR of a protein in a precipitated complex with a full-length antibody, J. Am. Chem. Soc. 136 (2014) 16800–16806, https://doi.org/10.1021/ja5069992. [21] P.S. Nadaud, J.J. Helmus, I. Sengupta, C.P. Jaroniec, Rapid acquisition of multidimensional solid-state NMR spectra of proteins facilitated by covalently bound paramagnetic tags, J. Am. Chem. Soc. 132 (2010) 9561–9563, https:// doi.org/10.1021/ja103545e. [22] P.S. Nadaud, J.J. Helmus, S.L. Kall, C.P. Jaroniec, Paramagnetic ions enable tuning of nuclear relaxation rates and provide long-range structural restraints in solid-state NMR of proteins, J. Am. Chem. Soc. 131 (2009) 8108–8120, https://doi.org/ 10.1021/ja900224z. [23] I. Sengupta, P.S. Nadaud, J.J. Helmus, C.D. Schwieters, C.P. Jaroniec, Protein fold determined by paramagnetic magic-angle spinning solid-state NMR spectroscopy, Nat. Chem. 4 (2012) 410–417, https://doi.org/10.1038/nchem.1299. [24] A. Perez, K. Gaalswyk, C.P. Jaroniec, J.L. MacCallum, High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints, Angew. Chem. Int. Ed. (2019), https://doi.org/10.1002/anie.201811895. [25] D. Mukhopadhyay, P.S. Nadaud, M.D. Shannon, C.P. Jaroniec, Rapid quantitative measurements of paramagnetic relaxation enhancements in Cu(II)-Tagged proteins by proton-detected solid-state NMR spectroscopy, J. Phys. Chem. Lett. 8 (2017) 5871–5877, https://doi.org/10.1021/acs.jpclett.7b02709. [26] M.J. Knight, A.J. Pell, I. Bertini, I.C. Felli, L. Gonnelli, R. Pierattelli, T. Herrmann, L. Emsley, G. Pintacuda, Structure and backbone dynamics of a microcrystalline metalloprotein by solid-state NMR, Proc. Natl. Acad. Sci. U.S.A. 109 (2012) 11095–11100, https://doi.org/10.1073/pnas.1204515109. [27] T. Theint, Y. Xia, P.S. Nadaud, D. Mukhopadhyay, C.D. Schwieters, K. Surewicz, W.K. Surewicz, C.P. Jaroniec, Structural studies of amyloid fibrils by paramagnetic solid-state nuclear magnetic resonance spectroscopy, J. Am. Chem. Soc. 140 (2018) 13161–13166, https://doi.org/10.1021/jacs.8b06758. [28] H. Tamaki, A. Egawa, K. Kido, T. Kameda, M. Kamiya, T. Kikukawa, T. Aizawa, T. Fujiwara, M. Demura, Structure determination of uniformly 13C, 15N labeled protein using qualitative distance restraints from MAS solid-state 13C-NMR observed paramagnetic relaxation enhancement, J. Biomol. NMR 64 (2016) 87–101, https://doi.org/10.1007/s10858-015-0010-0.

7

M. Tang, D. Lam

Solid State Nuclear Magnetic Resonance xxx (xxxx) xxx [68] S.G. Patching, Solid-state NMR structures of integral membrane proteins, Mol. Membr. Biol. 32 (2015) 156–178, https://doi.org/10.3109/ 09687688.2016.1139754. [69] M.E. Ward, S. Wang, S. Krishnamurthy, H. Hutchins, M. Fey, L.S. Brown, V. Ladizhansky, High-resolution paramagnetically enhanced solid-state NMR spectroscopy of membrane proteins at fast magic angle spinning, J. Biomol. NMR 58 (2014) 37–47, https://doi.org/10.1007/s10858-013-9802-2. [70] S.J. Ullrich, S. H€ olper, C. Glaubitz, Paramagnetic doping of a 7TM membrane protein in lipid bilayers by Gd3þ-complexes for solid-state NMR spectroscopy, J. Biomol. NMR 58 (2014) 27–35, https://doi.org/10.1007/s10858-013-9800-4. [71] S.N. Koroloff, D.M. Tesch, E.O. Awosanya, A.A. Nevzorov, Sensitivity enhancement for membrane proteins reconstituted in parallel and perpendicular oriented bicelles obtained by using repetitive cross-polarization and membrane-incorporated free radicals, J. Biomol. NMR 67 (2017) 135–144, https://doi.org/10.1007/s10858017-0090-0. [72] K. Yamamoto, J. Xu, K.E. Kawulka, J.C. Vederas, A. Ramamoorthy, Use of a copperchelated lipid speeds up NMR measurements from membrane proteins, J. Am. Chem. Soc. 132 (2010) 6929–6931, https://doi.org/10.1021/ja102103n. [73] M. Tang, D.A. Berthold, C.M. Rienstra, Solid-state NMR of a large membrane protein by paramagnetic relaxation enhancement, J. Phys. Chem. Lett. 2 (2011) 1836–1841, https://doi.org/10.1021/jz200768r. [74] K. Yamamoto, M.A. Caporini, S. Im, L. Waskell, A. Ramamoorthy, Shortening spinlattice relaxation using a copper-chelated lipid at low-temperatures - a magic angle spinning solid-state NMR study on a membrane-bound protein, J. Magn. Reson. 237 (2013) 175–181, https://doi.org/10.1016/j.jmr.2013.10.017. [75] S. Wang, T. Gopinath, G. Veglia, Application of paramagnetic relaxation enhancements to accelerate the acquisition of 2D and 3D solid-state NMR spectra of oriented membrane proteins, Methods 138–139 (2018) 54–61, https://doi.org/ 10.1016/j.ymeth.2017.12.017. [76] C.A.P. Whittaker, S.G. Patching, M. Esmann, D.A. Middleton, Ligand orientation in a membrane-embedded receptor site revealed by solid-state NMR with paramagnetic relaxation enhancement, Org. Biomol. Chem. 13 (2015) 2664–2668, https://doi.org/10.1039/c4ob02427c. [77] T. Wiegand, D. Lacabanne, K. Keller, R. Cadalbert, L. Lecoq, M. Yulikov, L. Terradot, G. Jeschke, B.H. Meier, A. B€ ockmann, Solid-state NMR and EPR spectroscopy of Mn2þ -substituted ATP-fueled protein engines, Angew Chem. Int. Ed. Engl. 56 (2017) 3369–3373, https://doi.org/10.1002/anie.201610551. [78] S. Wang, R.A. Munro, L. Shi, I. Kawamura, T. Okitsu, A. Wada, S.-Y. Kim, K.H. Jung, L.S. Brown, V. Ladizhansky, Solid-state NMR spectroscopy structure determination of a lipid-embedded heptahelical membrane protein, Nat. Methods 10 (2013) 1007–1012, https://doi.org/10.1038/nmeth.2635. [79] S.H. Park, S. Berkamp, J. Radoicic, A.A. De Angelis, S.J. Opella, Interaction of monomeric interleukin-8 with CXCR1 mapped by proton-detected fast MAS solidstate NMR, Biophys. J. 113 (2017) 2695–2705, https://doi.org/10.1016/ j.bpj.2017.09.041. [80] G. Jaipuria, K. Giller, A. Leonov, S. Becker, M. Zweckstetter, Insights into cholesterol/membrane protein interactions using paramagnetic solid-state NMR, Chem. Eur J. 24 (2018) 17606–17611, https://doi.org/10.1002/chem.201804550. [81] Y. Su, R. Mani, M. Hong, Asymmetric insertion of membrane proteins in lipid bilayers by solid-state NMR paramagnetic relaxation enhancement: a cellpenetrating Peptide example, J. Am. Chem. Soc. 130 (2008) 8856–8864, https:// doi.org/10.1021/ja802383t. [82] S. Maltsev, S.M. Hudson, I.D. Sahu, L. Liu, G.A. Lorigan, Solid-state NMR 31P paramagnetic relaxation enhancement membrane protein immersion depth measurements, J. Phys. Chem. B 118 (2014) 4370–4377, https://doi.org/10.1021/ jp500267y.

[53] J.B. Addison, W.S. Weber, Q. Mou, N.N. Ashton, R.J. Stewart, G.P. Holland, J.L. Yarger, Reversible assembly of β-sheet nanocrystals within caddisfly silk, Biomacromolecules 15 (2014) 1269–1275, https://doi.org/10.1021/bm401822p. [54] A. Bertarello, T. Schubeis, C. Fuccio, E. Ravera, M. Fragai, G. Parigi, L. Emsley, G. Pintacuda, C. Luchinat, Paramagnetic properties of a crystalline iron-sulfur protein by magic-angle spinning NMR spectroscopy, Inorg. Chem. 56 (2017) 6624–6629, https://doi.org/10.1021/acs.inorgchem.7b00674. [55] J.M. Courtney, Q. Ye, A.E. Nesbitt, M. Tang, M.D. Tuttle, E.D. Watt, K.M. Nuzzio, L.J. Sperling, G. Comellas, J.R. Peterson, J.H. Morrissey, C.M. Rienstra, Experimental protein structure verification by scoring with a single, unassigned NMR spectrum, Structure 23 (2015) 1958–1966, https://doi.org/10.1016/ j.str.2015.07.019. [56] J.-X. Lu, W. Qiang, W.-M. Yau, C.D. Schwieters, S.C. Meredith, R. Tycko, Molecular structure of β-amyloid fibrils in alzheimer's disease brain tissue, Cell 154 (2013) 1257–1268, https://doi.org/10.1016/j.cell.2013.08.035. [57] M.T. Colvin, R. Silvers, Q.Z. Ni, T.V. Can, I. Sergeyev, M. Rosay, K.J. Donovan, B. Michael, J. Wall, S. Linse, R.G. Griffin, Atomic resolution structure of monomorphic Aβ42 amyloid fibrils, J. Am. Chem. Soc. 138 (2016) 9663–9674, https://doi.org/10.1021/jacs.6b05129. [58] M.A. W€ alti, F. Ravotti, H. Arai, C.G. Glabe, J.S. Wall, A. B€ ockmann, P. Güntert, B.H. Meier, R. Riek, Atomic-resolution structure of a disease-relevant Aβ(1-42) amyloid fibril, Proc. Natl. Acad. Sci. U.S.A. 113 (2016) E4976–E4984, https:// doi.org/10.1073/pnas.1600749113. [59] C. Wasmer, A. Lange, H. Van Melckebeke, A.B. Siemer, R. Riek, B.H. Meier, Amyloid fibrils of the HET-s(218-289) prion form a beta solenoid with a triangular hydrophobic core, Science 319 (2008) 1523–1526, https://doi.org/10.1126/ science.1151839. [60] M.D. Tuttle, G. Comellas, A.J. Nieuwkoop, D.J. Covell, D.A. Berthold, K.D. Kloepper, J.M. Courtney, J.K. Kim, A.M. Barclay, A. Kendall, W. Wan, G. Stubbs, C.D. Schwieters, V.M.Y. Lee, J.M. George, C.M. Rienstra, Solid-state NMR structure of a pathogenic fibril of full-length human α-synuclein, Nat. Struct. Mol. Biol. 23 (2016) 409–415, https://doi.org/10.1038/nsmb.3194. [61] K.R. Thurber, R. Tycko, Biomolecular solid state NMR with magic-angle spinning at 25 K, J. Magn. Reson. 195 (2008) 179–186, https://doi.org/10.1016/ j.jmr.2008.09.015. [62] S. Parthasarathy, F. Long, Y. Miller, Y. Xiao, D. McElheny, K. Thurber, B. Ma, R. Nussinov, Y. Ishii, Molecular-level examination of Cu2þ binding structure for amyloid fibrils of 40-residue alzheimer's β by solid-state NMR spectroscopy, J. Am. Chem. Soc. 133 (2011) 3390–3400, https://doi.org/10.1021/ja1072178. [63] S. Parthasarathy, B. Yoo, D. McElheny, W. Tay, Y. Ishii, Capturing a reactive state of amyloid aggregates, J. Biol. Chem. 289 (2014) 9998–10010, https://doi.org/ 10.1074/jbc.M113.511345. [64] D. Aucoin, Y. Xia, T. Theint, P.S. Nadaud, K. Surewicz, W.K. Surewicz, C.P. Jaroniec, Protein-solvent interfaces in human Y145Stop prion protein amyloid fibrils probed by paramagnetic solid-state NMR spectroscopy, J. Struct. Biol. 206 (2018) 36–42, https://doi.org/10.1016/j.jsb.2018.04.002. [65] Y. Li, C. Zhao, F. Luo, Z. Liu, X. Gui, Z. Luo, X. Zhang, D. Li, C. Liu, X. Li, Amyloid fibril structure of α-synuclein determined by cryo-electron microscopy, Cell Res. 28 (2018) 897–903, https://doi.org/10.1038/s41422-018-0075-x. [66] B. Li, P. Ge, K.A. Murray, P. Sheth, M. Zhang, G. Nair, M.R. Sawaya, W.S. Shin, D.R. Boyer, S. Ye, D.S. Eisenberg, Z.H. Zhou, L. Jiang, Cryo-EM of full-length α-synuclein reveals fibril polymorphs with a common structural kernel, Nat. Commun. 9 (2018) 3609, https://doi.org/10.1038/s41467-018-05971-2. [67] R. Guerrero-Ferreira, N.M. Taylor, D. Mona, P. Ringler, M.E. Lauer, R. Riek, M. Britschgi, H. Stahlberg, Cryo-EM structure of alpha-synuclein fibrils, Elife 7 (2018), https://doi.org/10.7554/eLife.36402.

8