Improved analysis of NMR chemical shift perturbations through an error estimation method.

In solution NMR, chemical shift perturbation (CSP) experiments are widely employed to study intermolecular interactions. However, excluding the nonsignificant peak shift is difficult because little is known about errors in CSP. Here, to address this issue, we introduce a method for estimating errors in CSP based on the noise level. First, we developed a technique that involves line shape fitting to estimate errors in peak position via Monte Carlo simulations. Second, this technique was applied to estimate errors in CSP. In intermolecular interaction analysis of VAP-A with SNX2, error estimation of CSP enabled the evaluation of small but significant changes in peak position and yielded detailed insights that are unattainable with conventional CSP analysis. Third, this technique was successfully applied to estimate errors in residual dipolar couplings. In conclusion, our error estimation method improves CSP analysis by excluding the nonsignificant peak shift.

Efficient and economic protein labeling for NMR in mammalian expression systems: Application to a preT-cell and T-cell receptor protein.

Protein nuclear magnetic resonance (NMR) spectroscopy relies on the ability to isotopically label polypeptides, which is achieved through heterologous expression in various host organisms. Most commonly, Escherichia coli is employed by leveraging isotopically substituted ammonium and glucose to uniformly label proteins with 15N and 13C, respectively. Moreover, E. coli can grow and express proteins in uniformly deuterium-substituted water (D2O), a strategy useful for experiments targeting high molecular weight proteins. Unfortunately, many proteins, particularly those requiring specific posttranslational modifications like disulfide bonding or glycosylation for proper folding and/or function, cannot be readily expressed in their functional forms using E. coli-based expression systems. One such class of proteins includes T-cell receptors and their related preT-cell receptors. In this study, we present an expression system for isotopic labeling of proteins using a nonadherent human embryonic kidney cell line, Expi293F, and a specially designed media. We demonstrate the application of this platform to the ß subunit common to both receptors. In addition, we show that this expression system and media can be used to specifically label amino acids Phe, Ile, Val, and Leu in this system, utilizing an amino acid-specific labeling protocol that allows targeted incorporation at high efficiency without significant isotopic scrambling. We demonstrate that this system can also be used to express proteins with fluorinated amino acids. We were routinely able to obtain an NMR sample with a concentration of 200 µM from 30 mL of culture media, utilizing less than 20 mg of the labeled amino acids.

A comprehensive assessment of selective amino acid 15N-labeling in human embryonic kidney 293 cells for NMR spectroscopy.

A large proportion of human proteins contain post-translational modifications that cannot be synthesized by prokaryotes. Thus, mammalian expression systems are often employed to characterize structure/function relationships using NMR spectroscopy. Here we define the selective isotope labeling of secreted, post-translationally modified proteins using human embryonic kidney (HEK)293 cells. We determined that alpha-[15N]- atoms from 10 amino acids experience minimal metabolic scrambling (C, F, H, K, M, N, R, T, W, Y). Two more interconvert to each other (G, S). Six others experience significant scrambling (A, D, E, I, L, V). We also demonstrate that tuning culture conditions suppressed V and I scrambling. These results define expectations for 15N-labeling in HEK293 cells.

Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.

Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.

Solid-state 1H NMR-based metabolomics assessment of tributylin effects in zebrafish bone.

Tributyltin (TBT), an endocrine disruptor is used globally in agribusiness and industries as biocides, heat stabilizers, and in chemical catalysis. It is known for its deleterious effects on bone by negatively impacting the functions of osteoblasts, osteoclasts and mesenchymal stem cells. However, the impact of TBT on the metabolomics profile in bone is not yet studied. Here, we demonstrate alterations in chemical metabolomics profiles measured by solid state 1H nuclear magnetic resonance (1H NMR) spectroscopy in zebrafish bone following tributyltin (TBT) treatment. TBT of 0, 100, 200, 300, 400 and 500 µg/L were exposed to zebrafish. From this, zebrafish bone has subjected for further metabolomics profiling. Samples were measured via one-dimensional (1D) solvent -suppressed and T2- filtered methods with in vivo zebrafish metabolites. A dose dependent alteration in the metabolomics profile was observed and results indicated a disturbed aminoacid metabolism, TCA cycle, and glycolysis. We found a significant alteration in the levels of glutamate, glutamine, glutathione, trimethylamine N-oxide (TMAO), and other metabolites. This investigation hints us the deleterious effects of TBT on zebrafish bone enabling a comprehensive understanding of metabolomics profile and is expected to play a crucial role in understanding the deleterious effects of various endocrine disruptor on bone.

NMR Spectroscopy for Protein Higher Order Structure Similarity Assessment in Formulated Drug Products.

Peptide and protein drug molecules fold into higher order structures (HOS) in formulation and these folded structures are often critical for drug efficacy and safety. Generic or biosimilar drug products (DPs) need to show similar HOS to the reference product. The solution NMR spectroscopy is a non-invasive, chemically and structurally specific analytical method that is ideal for characterizing protein therapeutics in formulation. However, only limited NMR studies have been performed directly on marketed DPs and questions remain on how to quantitively define similarity. Here, NMR spectra were collected on marketed peptide and protein DPs, including calcitonin-salmon, liraglutide, teriparatide, exenatide, insulin glargine and rituximab. The 1D 1H spectral pattern readily revealed protein HOS heterogeneity, exchange and oligomerization in the different formulations. Principal component analysis (PCA) applied to two rituximab DPs showed consistent results with the previously demonstrated similarity metrics of Mahalanobis distance (DM) of 3.3. The 2D 1H-13C HSQC spectral comparison of insulin glargine DPs provided similarity metrics for chemical shift difference (Δδ) and methyl peak profile, i.e., 4 ppb for 1H, 15 ppb for 13C and 98% peaks with equivalent peak height. Finally, 2D 1H-15N sofast HMQC was demonstrated as a sensitive method for comparison of small protein HOS. The application of NMR procedures and chemometric analysis on therapeutic proteins offer quantitative similarity assessments of DPs with practically achievable similarity metrics.

LC and NMR Studies for Identification and Characterization of Degradation Byproducts of Olmesartan Acid, Elucidation of Their Degradation Pathway and Ecotoxicity Assessment.

The discovery of various sartans, which are among the most used antihypertensive drugs in the world, is increasingly frequent not only in wastewater but also in surface water and, in some cases, even in drinking or groundwater. In this paper, the degradation pathway of olmesartan acid, one of the most used sartans, was investigated by simulating the chlorination process normally used in a wastewater treatment plant to reduce similar emerging pollutants. The structures of nine isolated degradation byproducts (DPs), eight of which were isolated for the first time, were separated via chromatography column and HPLC methods, identified by combining nuclear magnetic resonance and mass spectrometry, and justified by a proposed mechanism of formation beginning from the parent drug. Ecotoxicity tests on olmesartan acid and its nine DPs showed that 50% of the investigated byproducts inhibited the target species Aliivibrio fischeri and Raphidocelis subcapitata, causing functional decreases of 18% and 53%, respectively.

Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS).

S100B, a biomarker of malignant melanoma, interacts with the p53 protein and diminishes its tumor suppressor function, which makes this S100 family member a promising therapeutic target for treating malignant melanoma. However, it is a challenge to design inhibitors that are specific for S100B in melanoma versus other S100-family members that are important for normal cellular activities. For example, S100A1 is most similar in sequence and structure to S100B, and this S100 protein is important for normal skeletal and cardiac muscle function. Therefore, a combination of NMR and computer aided drug design (CADD) was used to initiate the design of specific S100B inhibitors. Fragment-based screening by NMR, also termed "SAR by NMR," is a well-established method, and was used to examine spectral perturbations in 2D [1H, 15N]-HSQC spectra of Ca2+-bound S100B and Ca2+-bound S100A1, side-by-side, and under identical conditions for comparison. Of the 1000 compounds screened, two were found to be specific for binding Ca2+-bound S100A1 and four were found to be specific for Ca2+-bound S100B, respectively. The NMR spectral perturbations observed in these six data sets were then used to model how each of these small molecule fragments showed specificity for one S100 versus the other using a CADD approach termed Site Identification by Ligand Competitive Saturation (SILCS). In summary, the combination of NMR and computational approaches provided insight into how S100A1 versus S100B bind small molecules specifically, which will enable improved drug design efforts to inhibit elevated S100B in melanoma. Such a fragment-based approach can be used generally to initiate the design of specific inhibitors for other highly homologous drug targets.

Assessment of AMBER Force Fields for Simulations of ssDNA.

Single-stranded DNA (ssDNA) plays an important role in biological processes and is used in DNA nanotechnology and other novel applications. Many important research questions can be addressed with molecular simulations of ssDNA molecules; however, no dedicated force field for ssDNA has been developed, and there is limited experimental information about ssDNA structures. This study assesses the accuracy and applicability of existing Amber force fields for all-atom simulations of ssDNA, such as ff99, bsc0, bsc1, and OL15, in implicit and explicit solvents via comparison to available experimental data, such as Forster resonance energy transfer and small angle X-ray scattering. We observed that some force fields agree better with experiments than others mainly due to the difference in parameterization of the propensity for hydrogen bonding and base stacking. Overall, the Amber ff99 force field in the IGB5 or IGB8 implicit solvent and the bsc1 force field in the explicit TIP3P solvent had the best agreement with experiment.

Voxel-wise partial volume correction method for accurate estimation of tissue sodium concentration in 23 Na-MRI at 7 T.

Sodium is crucial for the maintenance of cell physiology, and its regulation of the sodium-potassium pump has implications for various neurological conditions. The distribution of sodium concentrations in tissue can be quantitatively evaluated by means of sodium MRI (23 Na-MRI). Despite its usefulness in diagnosing particular disease conditions, tissue sodium concentration (TSC) estimated from 23 Na-MRI can be strongly biased by partial volume effects (PVEs) that are induced by broad point spread functions (PSFs) as well as tissue fraction effects. In this work, we aimed to propose a robust voxel-wise partial volume correction (PVC) method for 23 Na-MRI. The method is based on a linear regression (LR) approach to correct for tissue fraction effects, but it utilizes a 3D kernel combined with a modified least trimmed square (3D-mLTS) method in order to minimize regression-induced inherent smoothing effects. We acquired 23 Na-MRI data with conventional Cartesian sampling at 7 T, and spill-over effects due to the PSF were considered prior to correcting for tissue fraction effects using 3D-mLTS. In the simulation, we found that the TSCs of gray matter (GM) and white matter (WM) were underestimated by 20% and 11% respectively without correcting tissue fraction effects, but the differences between ground truth and PVE-corrected data after the PVC using the 3D-mLTS method were only approximately 0.6% and 0.4% for GM and WM, respectively. The capability of the 3D-mLTS method was further demonstrated with in vivo 23 Na-MRI data, showing significantly lower regression errors (ie root mean squared error) as compared with conventional LR methods (p < 0.001). The results of simulation and in vivo experiments revealed that 3D-mLTS is superior for determining under- or overestimated TSCs while preserving anatomical details. This suggests that the 3D-mLTS method is well suited for the accurate determination of TSC, especially in small focal lesions associated with pathological conditions.

Characterization of low-cost glycolipoprotein biosurfactant produced by Lactobacillus plantarum 60 FHE isolated from cheese samples using food wastes through response surface methodology and its potential as antimicrobial, antiviral, and anticancer activities.

Considering the need of new lactic acid bacteria (LAB) for the production of novel biosurfactant (BS) molecules, the current study brings out a new insight on the exploration of cheese samples for BS producers and process optimization for industrial applications. In view of this, Lactobacillus plantarum 60FHE, Lactobacillus paracasei 75FHE, and Lactobacillus paracasei 77FHE were selected as the most operative strains. The biosurfactants (BSs) described as glycolipoproteins via Fourier-transform infrared spectroscopy (FTIR) exhibited antimicrobial activity against the food-borne pathogens. L. plantarum 60FHE BS showed an anticancer activity against colon carcinoma cells and had a week antiviral activity against Hepatitis A virus. Furthermore, glycolipoprotein production was enhanced by 1.42-fold through the development of an optimized process using central composite design (CCD). Emulsifying activities were stable after 60-min incubation from 4 to 120 °C, at pH 2-12, and after the addition of NaCl (2-14%). Characterization by nuclear magnetic resonance spectroscopy (1H NMR) revealed that BS produced from strain 60FHE was glycolipoprotein. L. plantarum produced mixed BSs determined by Liquid Chromatography/Mass Spectrometry (LC-MS). Thus, indicating that BS was applied as a microbial food prevention and biomedical. Also, L. plantarum 60FHE BS was achieved with the use of statistical optimization on inexpensive food wastes.

Assessment of NR4A Ligands That Directly Bind and Modulate the Orphan Nuclear Receptor Nurr1.

Nurr1/NR4A2 is an orphan nuclear receptor transcription factor implicated as a drug target for neurological disorders including Alzheimer's and Parkinson's diseases. Previous studies identified small-molecule NR4A nuclear receptor modulators, but it remains unclear if these ligands affect transcription via direct binding to Nurr1. We assessed 12 ligands reported to affect NR4A activity for Nurr1-dependent and Nurr1-independent transcriptional effects and the ability to bind the Nurr1 ligand-binding domain (LBD). Protein NMR structural footprinting data show that amodiaquine, chloroquine, and cytosporone B bind the Nurr1 LBD; ligands that do not bind include C-DIM12, celastrol, camptothecin, IP7e, isoalantolactone, ethyl 2-[2,3,4-trimethoxy-6-(1-octanoyl)phenyl]acetate (TMPA), and three high-throughput screening hit derivatives. Importantly, ligands that modulate Nurr1 transcription also show Nurr1-independent effects on transcription in a cell type-specific manner, indicating that care should be taken when interpreting the functional response of these ligands in transcriptional assays. These findings should help focus medicinal chemistry efforts that desire to optimize Nurr1-binding ligands.

Accurate and cost-effective NMR chemical shift predictions for proteins using a molecules-in-molecules fragmentation-based method.

We have developed an efficient protocol using our two-layer Molecules-in-Molecules (MIM2) fragmentation-based quantum chemical method for the prediction of NMR chemical shifts of large biomolecules. To investigate the performance of our fragmentation approach and demonstrate its applicability, MIM-NMR calculations are first calibrated on a test set of six proteins. The MIM2-NMR method yields a mean absolute deviation (MAD) from unfragmented full molecule calculations of 0.01 ppm for 1H and 0.06 ppm for 13C chemical shifts. Thus, the errors from fragmentation are only about 3% of our target accuracy of ∼0.3 ppm for 1H and 2-3 ppm for 13C chemical shifts. To compare with experimental chemical shifts, a standard protocol is first derived using two smaller proteins 2LHY (176 atoms) and 2LI1 (146 atoms) for obtaining an appropriate protein structure for NMR chemical shift calculations. The effect of the solvent environment on the calculated NMR chemical shifts is incorporated through implicit, explicit, or explicit-implicit solvation models. The expensive first solvation shell calculations are replaced by a micro-solvation model in which only the immediate interaction between the protein and the explicit solvation environment is considered. A single explicit water molecule for each amine and amide proton is found to be sufficient to yield accurate results for 1H chemical shifts. The 1H and 13C NMR chemical shifts calculated using our protocol give excellent agreement with experiments for two larger proteins, 2MC5 (the helical part with 265 atoms) and 3UMK (33 residue slice with 547 atoms). Overall, our target accuracy of ∼0.3 ppm for 1H and ∼2-3 ppm for 13C has been achieved for the larger proteins. The proposed MIM-NMR method is accurate and computationally cost-effective and should be applicable to study a wide range of large proteins.

The assessment of three dimensional modelling design for single strand DNA aptamers for computational chemistry application.

Aptamers are oligonucleotides and peptides around 15-100 bases in length and are suitable as detection probes or as therapeutics molecules. There are growing interests in the aptamer screening approach through computational simulation methods. DNA and RNA modelling lacks of validation on their predicted 3D structures due to less number of validation tools, unlike protein structures. We suggest an approach to design the stem-loop/hairpin for the three dimensional structure of DNA aptamers through serial applications of computational prediction methods by comparing the simulated structures with the experimental data deposited in PDB Data bank, followed by MD simulations. The result shows minimal structural differences were observed between the designed and the original NMR aptamers, and the stem-loop conformational structures were also retained during the MD thus suggesting the proposed aptamers designing methods are able to synthesize a high quality molecular structure of hairpin aptamers, comparable to the NMR structures.

Quantitative Protein Disorder Assessment Using NMR Chemical Shifts.

Disorder is vital for the biological function of many proteins. The huge diversity found in disorder composition and amplitude reflects the complexity and pluripotency of intrinsically disordered proteins (IDPs). The first step toward a better understanding of IDPs is a quantitative and position-specific experimental characterization, and nuclear magnetic resonance (NMR) spectroscopy has emerged as the method of first choice. Here, we describe how to quantitatively assess the local balance between order and disorder in proteins by utilizing the Chemical shift Z-score for assessing Order/Disorder (CheZOD Z-score). This order/disorder metric is computed from the difference between experimentally determined NMR chemical shifts and computed random coil reference values. We explain in detail how CheZOD Z-scores are calculated fast and easily, either by using a python executable or by data submission to a server.

Bioplastic (poly-3-hydroxybutyrate) production by the marine bacterium Pseudodonghicola xiamenensis through date syrup valorization and structural assessment of the biopolymer.

Biobased degradable plastics have received significant attention owing to their potential application as a green alternative to synthetic plastics. A dye-based procedure was used to screen poly-3-hydroxybutyrate (PHB)-producing marine bacteria isolated from the Red Sea, Saudi Arabia. Among the 56 bacterial isolates, Pseudodonghicola xiamenensis, identified using 16S rRNA gene analyses, accumulated the highest amount of PHB. The highest PHB production by P. xiamenensis was achieved after 96 h of incubation at pH 7.5 and 35 °C in the presence of 4% NaCl, and peptone was the preferred nitrogen source. The use of date syrup at 4% (w/v) resulted in a PHB concentration of 15.54 g/L and a PHB yield of 38.85% of the date syrup, with a productivity rate of 0.162 g/L/h, which could substantially improve the production cost. Structural assessment of the bioplastic by Fourier transform infrared spectroscopy and nuclear magnetic resonance spectroscopy revealed the presence of methyl, hydroxyl, methine, methylene, and ester carbonyl groups in the extracted polymer. The derivative products of butanoic acid estimated by gas chromatography-mass spectrometry [butanoic acid, 2-amino-4-(methylseleno), hexanoic acid, 4-methyl-, methyl ester, and hexanedioic acid, monomethyl ester] confirmed the structure of PHB. The present results are the first report on the production of a bioplastic by P. xiamenensis, suggesting that Red Sea habitats are a potential biological reservoir for novel bioplastic-producing bacteria.

Assessment of the Higher-Order Structure of Formulated Monoclonal Antibody Therapeutics by 2D Methyl Correlated NMR and Principal Component Analysis.

Characterization of the higher-order structure (HOS) of protein therapeutics, and in particular of monoclonal antibodies, by 2D 1 H-13 C methyl correlated NMR has been demonstrated as precise and robust. Such characterization can be greatly enhanced when collections of spectra are analyzed using multivariate approaches such as principal component analysis (PCA), allowing for the detection and identification of small structural differences in drug substance that may otherwise fall below the limit of detection of conventional spectral analysis. A major limitation to this approach is the presence of aliphatic signals from formulation or excipient components, which result in spectral interference with the protein signal of interest; however, the recently described Selective Excipient Reduction and Removal (SIERRA) filter greatly reduces this issue. Here we will outline how basic 2D 1 H-13 C methyl-correlated NMR may be combined with the SIERRA approach to collect 'clean' NMR spectra of formulated monoclonal antibody therapeutics (i.e., drug substance spectra free of interfering component signals), and how series of such spectra may be used for HOS characterization by direct PCA of the series spectral matrix. © 2020 U.S. Government. Basic Protocol 1: NMR data acquisition Basic Protocol 2: Full spectral matrix data processing and analysis Support Protocol: Data visualization and cluster analysis.

13C NMR Relaxation Analysis of Protein GB3 for the Assessment of Side Chain Dynamics Predictions by Current AMBER and CHARMM Force Fields.

Molecular simulations with seven current AMBER- and CHARMM-based force fields yield markedly differing internal bond vector autocorrelation function predictions for many of the 223 methine and methylene H-C bonds of the 56-residue protein GB3. To enable quantification of accuracy, 13C R1, R2, and heteronuclear NOE relaxation rates have been determined for the methine and stereochemically assigned methylene Cα and Cß positions. With only three experimental relaxation values for each bond vector, central to this analysis is the accuracy with which MD-derived autocorrelation curves can be represented by a 3-parameter equation which, in turn, maps onto the NMR relaxation values. In contrast to the more widely used extended Lipari-Szabo order parameter representation, 95% of these MD-derived internal autocorrelation curves for GB3 can be fitted to within 1.0% rmsd over the time frame from 30 ps to 4 ns by a biexponential Larmor frequency-selective representation (LF-S2). Applying the LF-S2 representation to the experimental relaxation rates and uncertainties serves to determine the boundary range for the autocorrelation function of each bond vector consistent with the experimental data. Not surprisingly, all seven force fields predict the autocorrelation functions for the more motionally restricted 1Hα-13Cα and 1Hß-13Cß bond vectors with reasonable accuracy. However, for the 1Hß-13Cß bond vectors exhibiting aggregate order parameter S2 values less than 0.85, only 1% of the MD-derived predictions lie with 1 σ of the experimentally determined autocorrelation functions and only 7% within 2 σ. On the other hand, substantial residue type-specific improvements in predictive performance were observed among the recent AMBER force fields. This analysis indicates considerable potential for the use of 13C relaxation measurements in guiding the optimization of the side chain dynamics characteristics of protein molecular simulations.

Sensory Assessment of Fish and Chicken Protein Hydrolysates. Evaluation of NMR Metabolomics Profiling as a New Prediction Tool.

Nuclear magnetic resonance (NMR) metabolomics profiling was evaluated as a new tool in sensory assessment of protein hydrolysates. Hydrolysates were produced on the basis of different raw materials (cod, salmon, and chicken), enzymes (Food Pro PNL and Bromelain), and hydrolysis time (10 and 50 min). The influence of raw material and hydrolysis parameters on sensory attributes was determined by traditional descriptive sensory analysis and 1H NMR spectroscopy. The raw material had a major influence on the attribute intensity and metabolite variation, followed by enzyme and hydrolysis time. However, the formation of bitter taste was not affected by the raw material. Partial least-squares regression (PLSR) on 1H NMR and sensory data provided good models (Q2 = 0.55-0.89) for 11 of the 17 evaluated attributes, including bitterness. Significant metabolite-attribute associations were identified. The study confirms the potential prediction of the sensory properties of protein hydrolysates from cod, salmon, and chicken based on 1H NMR metabolomics profiling.

Use of single-molecule time-series data for refining conformational dynamics in molecular simulations.

Atomically detailed description of conformational dynamics in biomolecules is often essential to understand biological functions. Combining experimental measurements with molecular simulations significantly improves the outcome. Ensemble refinements, where the simulations are utilized to refine ensemble averaged data in NMR, SAXS, or cryo-EM, are a popular approach in integrative structural biology. Single-molecule time-series data contain rich temporal information of biomolecular dynamics. However, direct usage of the time-series data together with molecular simulations is just beginning. Here, we review data-assimilation approaches linking molecular simulations with experimental time-series data and discuss current limitations and potential applications of this approach in integrative structural biology.