Removal of a time barrier for high-resolution multidimensional NMR spectroscopy

Backbone 1H, 13C and 15N resonance assignment of the ubiquitin specific protease 7 catalytic domain (residues 208-554) in complex with a small molecule ligand

The backbone 1H, 13C and 15N resonance assignment of Ubiquitin Specific Protease 7 catalytic domain (residues 208-554) was performed in its complex with a small molecule ligand and in its apo form as a reference. The amide 1H-15N signal intensities were boosted by an amide hydrogen exchange protocol, where expressed 2H, 13C, 15N-labeled protein was unfolded and re-folded to ensure exchange of amide deuterons to protons. The resonance assignments were used to determine chemical shift perturbations on ligand binding, which are consistent with the binding site observed by crystallography.

Structural basis of substrate recognition and allosteric activation of the proapoptotic mitochondrial HtrA2 protease

The mitochondrial serine protease HtrA2 is a human homolog of the Escherichia coli Deg-proteins exhibiting chaperone and proteolytic roles. HtrA2 is involved in both apoptotic regulation via its ability to degrade inhibitor-of-apoptosis proteins (IAPs), as well as in cellular maintenance as part of the cellular protein quality control machinery, by preventing the possible toxic accumulation of aggregated proteins. In this study, we use advanced solution NMR spectroscopy methods combined with biophysical characterization and biochemical assays to elucidate the crucial role of the substrate recognizing PDZ domain. This domain regulates the protease activity of HtrA2 by triggering an intricate allosteric network involving the regulatory loops of the protease domain. We further show that divalent metal ions can both positively and negatively modulate the activity of HtrA2, leading to a refined model of HtrA2 regulation within the apoptotic pathway.

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.

A Sparse Model-Inspired Deep Thresholding Network for Exponential Signal Reconstruction-Application in Fast Biological Spectroscopy

The nonuniform sampling (NUS) is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms. Faithful reconstruction from partially sampled exponentials is highly expected in general signal processing and many applications. Deep learning (DL) has shown astonishing potential in this field, but many existing problems, such as lack of robustness and explainability, greatly limit its applications. In this work, by combining the merits of the sparse model-based optimization method and data-driven DL, we propose a DL architecture for spectra reconstruction from undersampled data, called MoDern. It follows the iterative reconstruction in solving a sparse model to build the neural network, and we elaborately design a learnable soft-thresholding to adaptively eliminate the spectrum artifacts introduced by undersampling. Extensive results on both synthetic and biological data show that MoDern enables more robust, high-fidelity, and ultrafast reconstruction than the state-of-the-art methods. Remarkably, MoDern has a small number of network parameters and is trained on solely synthetic data while generalizing well to biological data in various scenarios. Furthermore, we extend it to an open-access and easy-to-use cloud computing platform (XCloud-MoDern), contributing a promising strategy for further development of biological applications.

Exponential Signal Reconstruction With Deep Hankel Matrix Factorization

Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials' reconstruction method with the low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods.

Non-uniform sampling of similar NMR spectra and its application to studies of the interaction between alpha-synuclein and liposomes

The accelerated acquisition of multidimensional NMR spectra using sparse non-uniform sampling (NUS) has been widely adopted in recent years. The key concept in NUS is that a major part of the data is omitted during measurement, and then reconstructed using, for example, compressed sensing (CS) methods. CS requires spectra to be compressible, that is, they should contain relatively few "significant" points. The more compressible the spectrum, the fewer experimental NUS points needed in order for it to be accurately reconstructed. In this paper we show that the CS processing of similar spectra can be enhanced by reconstructing only the differences between them. Accurate reconstruction can be obtained at lower sampling levels as the difference is sparser than the spectrum itself. In many situations this method is superior to "conventional" compressed sensing. We exemplify the concept of "difference CS" with one such case-the study of alpha-synuclein binding to liposomes and its dependence on temperature. To obtain information on temperature-dependent transitions between different states, we need to acquire several dozen spectra at various temperatures, with and without the presence of liposomes. Our detailed investigation reveals that changes in the binding modes of the alpha-synuclein ensemble are not only temperature-dependent but also show non-linear behavior in their transitions. Our proposed CS processing approach dramatically reduces the number of NUS points required and thus significantly shortens the experimental time.

M, Katona G. Survivin prevents the polycomb repressor complex 2 from methylating histone 3 lysine 27

This study investigates the role of survivin in epigenetic control of gene transcription through interaction with the polycomb repressive complex 2 (PRC2). PRC2 is responsible for silencing gene expression by trimethylating lysine 27 on histone 3. We observed differential expression of PRC2 subunits in CD4+ T cells with varying levels of survivin expression, and ChIP-seq results indicated that survivin colocalizes with PRC2 along DNA. Inhibition of survivin resulted in a significant increase in H3K27 trimethylation, implying that survivin prevents PRC2 from functioning. Peptide microarray showed that survivin interacts with peptides from PRC2 subunits, and machine learning revealed that amino acid composition contains relevant information for predicting survivin interaction. NMR and BLI experiments supported the interaction of survivin with PRC2 subunit EZH2. Finally, protein-protein docking revealed that the survivin-EZH2 interaction interface overlaps with catalytic residues of EZH2, potentially inhibiting its H3K27 methylation activity. These findings suggest that survivin inhibits PRC2 function.

nus-tool: A unified program for generating and analyzing sample schedules for nonuniformly sampled NMR experiments

Increases in digital resolution achieved by high-field NMR require increases in spectral width. Additionally, the ability to resolve two overlapping peaks requires a sufficiently long acquisition time. These constraints combine, so that achieving high resolution spectra on high-field magnets requires long experiment times when employing uniform sampling and Fourier Transform processing. These limitations may be addressed by using nonuniform sampling (NUS), but the complexity of the parameter space across the variety of available NUS schemes greatly hinders the establishment of optimal approaches and best practices. We address these challenges with nus-tool, which is a software package for generating and analyzing NUS schedules. The nus-tool software internally implements random sampling and exponentially biased sampling. Through pre-configured plug-ins, it also provides access to quantile sampling and Poisson gap sampling. The software computes the relative sensitivity, mean evolution time, point spread function, and peak-to-sidelobe ratio; all of which can be determined for a candidate sample schedule prior to running an experiment to verify expected sensitivity, resolution, and artifact suppression. The nus-tool package is freely available on the NMRbox platform through an interactive GUI and via the command line, which is especially useful for scripted workflows that investigate the effectiveness of various NUS schemes.

Disulfide-Bond-Induced Structural Frustration and Dynamic Disorder in a Peroxiredoxin from MAS NMR

Disulfide bond formation is fundamentally important for protein structure and constitutes a key mechanism by which cells regulate the intracellular oxidation state. Peroxiredoxins (PRDXs) eliminate reactive oxygen species such as hydrogen peroxide through a catalytic cycle of Cys oxidation and reduction. Additionally, upon Cys oxidation PRDXs undergo extensive conformational rearrangements that may underlie their presently structurally poorly defined functions as molecular chaperones. Rearrangements include high molecular-weight oligomerization, the dynamics of which are, however, poorly understood, as is the impact of disulfide bond formation on these properties. Here we show that formation of disulfide bonds along the catalytic cycle induces extensive μs time scale dynamics, as monitored by magic-angle spinning NMR of the 216 kDa-large Tsa1 decameric assembly and solution-NMR of a designed dimeric mutant. We ascribe the conformational dynamics to structural frustration, resulting from conflicts between the disulfide-constrained reduction of mobility and the desire to fulfill other favorable contacts.

NMR spectrum reconstruction as a pattern recognition problem

A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform sampling (NUS) schedule, the WNN benefits from pattern recognition of the corresponding point spread function (PSF) pattern produced by each spectral peak resulting in the highest quality and robust reconstruction of the NUS spectra as demonstrated in simulations and exemplified in this work on 2D ¹H-¹⁵N correlation spectra of three representative globular proteins with different sizes: Ubiquitin (8.6 kDa), Azurin (14 kDa), and Malt1 (44 kDa). The pattern recognition by WNN is also demonstrated for successful virtual homo-decoupling in a 2D methyl ¹H-¹³C - HMQC spectrum of MALT1. We demonstrate using WNN that prior knowledge about the NUS schedule, which so far was not been fully exploited, can be used for designing new powerful NMR processing techniques that surpass the existing algorithmic methods.

PplD is a de-N-acetylase of the cell wall linkage unit of streptococcal rhamnopolysaccharides

The cell wall of the human bacterial pathogen Group A Streptococcus (GAS) consists of peptidoglycan decorated with the Lancefield group A carbohydrate (GAC). GAC is a promising target for the development of GAS vaccines. In this study, employing chemical, compositional, and NMR methods, we show that GAC is attached to peptidoglycan via glucosamine 1-phosphate. This structural feature makes the GAC-peptidoglycan linkage highly sensitive to cleavage by nitrous acid and resistant to mild acid conditions. Using this characteristic of the GAS cell wall, we identify PplD as a protein required for deacetylation of linkage N-acetylglucosamine (GlcNAc). X-ray structural analysis indicates that PplD performs catalysis via a modified acid/base mechanism. Genetic surveys in silico together with functional analysis indicate that PplD homologs deacetylate the polysaccharide linkage in many streptococcal species. We further demonstrate that introduction of positive charges to the cell wall by GlcNAc deacetylation protects GAS against host cationic antimicrobial proteins.

Characterization of backbone dynamics using solution NMR spectroscopy to discern the functional plasticity of structurally analogous proteins

The comprehensive delineation of inherent dynamic motions embedded in proteins, which can be crucial for their functional repertoire, is often essential yet remains poorly understood in the majority of cases. In this protocol, we outline detailed descriptions of the necessary steps for employing solution NMR spectroscopy for the in-depth amino acid level understanding of backbone dynamics of proteins. We describe the application of the protocol on the structurally analogous Tudor domains with disparate functionalities as a model system. For complete details on the use and execution of this protocol, please refer to Kawale and Burmann (2021).

Structural basis of DegP protease temperature-dependent activation

Protein quality control is an essential cellular function mainly executed by a vast array of different proteases and molecular chaperones. One of the bacterial high temperature requirement A (HtrA) protein family members, the homo-oligomeric DegP protease, plays a crucial role in the Escherichia coli protein quality control machinery by removing unfolded proteins or preventing their aggregation and chaperoning them to their final folded state within the periplasm. DegP contains two regulatory PDZ domains, which play key roles in substrate recognition and in the transformation of DegP between inactive hexameric and proteolytic active cage-like structures. Here, we analyze the interaction and dynamics of the DegP PDZ domains underlying this transformation by high-resolution NMR spectroscopy complemented with biochemical cleavage assays. We identify an interdomain molecular lock, which controls the interactions between the two PDZ domains, regulated by fine-tuned temperature-dependent protein dynamics, and which is potentially conserved in proteins harboring tandem PDZ domains.

Altered ceramide metabolism is a feature in the extracellular vesicle-mediated spread of alpha-synuclein in Lewy body disorders

Mutations in glucocerebrosidase (GBA) are the most prevalent genetic risk factor for Lewy body disorders (LBD)-collectively Parkinson's disease, Parkinson's disease dementia and dementia with Lewy bodies. Despite this genetic association, it remains unclear how GBA mutations increase susceptibility to develop LBD. We investigated relationships between LBD-specific glucocerebrosidase deficits, GBA-related pathways, and α-synuclein levels in brain tissue from LBD and controls, with and without GBA mutations. We show that LBD is characterised by altered sphingolipid metabolism with prominent elevation of ceramide species, regardless of GBA mutations. Since extracellular vesicles (EV) could be involved in LBD pathogenesis by spreading disease-linked lipids and proteins, we investigated EV derived from post-mortem cerebrospinal fluid (CSF) and brain tissue from GBA mutation carriers and non-carriers. EV purified from LBD CSF and frontal cortex were heavily loaded with ceramides and neurodegeneration-linked proteins including alpha-synuclein and tau. Our in vitro studies demonstrate that LBD EV constitute a "pathological package" capable of inducing aggregation of wild-type alpha-synuclein, mediated through a combination of alpha-synuclein-ceramide interaction and the presence of pathological forms of alpha-synuclein. Together, our findings indicate that abnormalities in ceramide metabolism are a feature of LBD, constituting a promising source of biomarkers, and that GBA mutations likely accelerate the pathological process occurring in sporadic LBD through endolysosomal deficiency.

Clustered sparsity and Poisson-gap sampling

Non-uniform sampling (NUS) is a popular way of reducing the amount of time taken by multidimensional NMR experiments. Among the various non-uniform sampling schemes that exist, the Poisson-gap (PG) schedules are particularly popular, especially when combined with compressed-sensing (CS) reconstruction of missing data points. However, the use of PG is based mainly on practical experience and has not, as yet, been explained in terms of CS theory. Moreover, an apparent contradiction exists between the reported effectiveness of PG and CS theory, which states that a "flat" pseudo-random generator is the best way to generate sampling schedules in order to reconstruct sparse spectra. In this paper we explain how, and in what situations, PG reveals its superior features in NMR spectroscopy. We support our theoretical considerations with simulations and analyses of experimental data from the Biological Magnetic Resonance Bank (BMRB). Our analyses reveal a previously unnoticed feature of many NMR spectra that explains the success of "blue-noise" schedules, such as PG. We call this feature "clustered sparsity". This refers to the fact that the peaks in NMR spectra are not just sparse but often form clusters in the indirect dimension, and PG is particularly suited to deal with such situations. Additionally, we discuss why denser sampling in the initial and final parts of the clustered signal may be useful.

FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling

In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple ¹³C_α-¹³C_β couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data.

Software for reconstruction of nonuniformly sampled NMR data

Nonuniform sampling (NUS) of multidimensional NMR experiments is a powerful tool to obtain high-resolution spectra with less instrument time. With NUS, only a subset of the points needed for conventional Fourier transformation is recorded, and sophisticated algorithms are needed to reconstruct the missing data points. During the last decade, several software packages implementing the reconstruction algorithms have emerged and been refined and now result in spectra of almost similar quality as spectra from conventionally recorded and processed data. However, from the number of literature references to the reconstruction methods, many more multidimensional NMR spectra could presumably be recorded with NUS. To help researchers considering to start using NUS for their NMR experiments, we here review 13 different reconstruction methods found in five software packages (CambridgeCS, hmsIST, MddNMR, NESTA-NMR, and SMILE). We have compared how the methods run with the provided example scripts for reconstructing a nonuniform sampled three-dimensional ¹⁵ N-NOESY-HSQC at sampling densities from 5% to 50%. Overall, the spectra are all of similar quality above 20% sampling density. Thus, without any particular knowledge about the details of the reconstruction algorithms, significant reduction in the experiment time can be achieved. Below 20% sampling density, the intensities of particular weak peaks start being affected. MddNMR's IST with virtual echo and the SMILE algorithms still reproduced the spectra with the highest accuracy of peak intensities.

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

Resolution enhancement in NMR spectra by deconvolution with compressed sensing reconstruction

NMR spectroscopy is one of the basic tools for molecular structure elucidation. Unfortunately, the resolution of the spectra is often limited by inter-nuclear couplings. The existing workarounds often alleviate the problem by trading it for another deficiency, such as spectral artefacts or difficult sample preparation and, thus, are rarely used. We suggest an approach using the coupling deconvolution in the framework of compressed sensing (CS) spectra processing that leads to a major increase in resolution, sensitivity, and overall quality of NUS reconstruction. A new mathematical description of the decoupling by deconvolution explains the effects of thermal noise and reveals a relation with the underlying assumption of the CS. The gain in resolution and sensitivity for challenging molecular systems is demonstrated for the key HNCA experiment used for protein backbone assignment applied to two large proteins: intrinsically disordered 441-residue Tau and a 509-residue globular bacteriophytochrome fragment. The approach will be valuable in a multitude of chemistry applications, where NMR experiments are compromised by the homonuclear scalar coupling.

UvrD helicase-RNA polymerase interactions are governed by UvrD's carboxy-terminal Tudor domain

All living organisms have to cope with the constant threat of genome damage by UV light and other toxic reagents. To maintain the integrity of their genomes, organisms developed a variety of DNA repair pathways. One of these, the Transcription Coupled DNA-Repair (TCR) pathway, is triggered by stalled RNA Polymerase (RNAP) complexes at DNA damage sites on actively transcribed genes. A recently elucidated bacterial TCR pathway employs the UvrD helicase pulling back stalled RNAP complexes from the damage, stimulating recruitment of the DNA-repair machinery. However, structural and functional aspects of UvrD's interaction with RNA Polymerase remain elusive. Here we used advanced solution NMR spectroscopy to investigate UvrD's role within the TCR, identifying that the carboxy-terminal region of the UvrD helicase facilitates RNAP interactions by adopting a Tudor-domain like fold. Subsequently, we functionally analyzed this domain, identifying it as a crucial component for the UvrD-RNAP interaction besides having nucleic-acid affinity.

Signaling Mechanism of Phytochromes in Solution

Phytochrome proteins guide the red/far-red photoresponse of plants, fungi, and bacteria. Crystal structures suggest that the mechanism of signal transduction from the chromophore to the output domains involves refolding of the so-called PHY tongue. It is currently not clear how the two other notable structural features of the phytochrome superfamily, the so-called helical spine and a knot in the peptide chain, are involved in photoconversion. Here, we present solution NMR data of the complete photosensory core module from Deinococcus radiodurans. Photoswitching between the resting and the active states induces changes in amide chemical shifts, residual dipolar couplings, and relaxation dynamics. All observables indicate a photoinduced structural change in the knot region and lower part of the helical spine. This implies that a conformational signal is transduced from the chromophore to the helical spine through the PAS and GAF domains. The discovered pathway underpins functional studies of plant phytochromes and may explain photosensing by phytochromes under biological conditions.

Prediction of the sweetening effect of Siraitia grosvenorii (luo han guo) fruits by two-dimensional quantitative NMR

During the last decade, dried fruits of Siraitia grosvenorii (luo han guo), also known as monk fruit, have become popular food ingredients. Luo han guo extracts, which are promoted as non-caloric natural sweeteners, are now incorporated into dietary supplements, soft drinks, and energy shakes. The compounds responsible for the sweetening effect are glycosylated cucurbitane-type triterpenoids, the so-called mogrosides. However, of the more than 40 known mogroside compounds, only 11-α-hydroxy-mogrosides exhibit a sweetening effect, whereas the other triterpenoids are non- or bitter-tasting. We have used two-dimensional quantitative NMR to determine selectively the content of 11-α-hydroxy-mogrosides in these dried fruits and thus to predict their sweetening potential. Homonuclear (H,H COSY) and heteronuclear (HSQC) methods were developed, validated, and compared. Both techniques were found suitable for the quality control of luo han guo fruits and extracts, the COSY experiment being advantageous with regard to accuracy, precision, and limit of quantification.

Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.

Confronting the Invisible: Assignment of Protein 1HN Chemical Shifts in Cases of Extreme Broadening

NMR studies of intrinsically disordered proteins (IDPs) at neutral pH values are hampered by the rapid exchange of backbone amide protons with solvent. Although exchange rates can be modulated by changes in pH, interactions between IDPs that lead to phase separation sometimes only occur at neutral pH values or higher, where backbone amide-based experiments fail. Here we describe a simple NMR experiment for measuring amide proton chemical shifts in cases where ¹H^N spectra cannot be obtained. The approach uses a weak ¹H B₁ field, searching for elusive ¹H^N resonance frequencies that become encoded in the intensities of cross-peaks in three-dimensional ¹H^α-detect spectra. Applications to the CAPRIN1 protein in both dilute- and phase-separated states highlight the utility of the method, establishing that accurate ¹H^N chemical shifts can be obtained even in cases where solvent hydrogen exchange rates are on the order of 1500 s^-1.

Escherichia coli O176 LPS structure and dynamics: A NMR spectroscopy and MD simulation study

A lipopolysaccharide (LPS) molecule is a key component of the bacterial outer membrane used to protect the bacterium and to interact with the environment. To gain insight into its function, the study of the LPS conformation and dynamics at the molecular and cellular levels is necessary, but these highly diverse and dynamic membrane-LPS systems are difficult to study. In this work, by using NMR spectroscopy and molecular dynamics (MD) simulations, we determined the conformational preferences of an E. coli O176 O-antigen polysaccharide at the atomic level. Moreover, we analyzed the use of non-uniform sampling (NUS) for the acquisition of high dynamic range spectra, like ¹H,¹H-NOESY NMR experiments. A comparison of the effective transglycosidic distances derived from conventional uniformly sampled and NUS ¹H,¹H-NOESY data showed high similarity under equal measuring time conditions. Furthermore, the experimentally derived internuclear distances of the O-antigen polysaccharide with ten repeating units (RUs) showed very good agreement to those calculated from the MD simulations of the same O-antigen polysaccharide in solution. Analysis of the LPS bilayer simulations with five and with ten RUs revealed that, although similar with respect to populated states in solution, the O-antigen in LPS bilayers had more extended chains as a result of spatial limitations due to close packing. Additional MD simulations of O-antigen polysaccharides from E. coli O6 (branched repeating unit) and O91 (negatively charged linear repeating unit) in solution and LPS bilayers were performed and compared to those of O176 (linear polymer). For all three O-antigens, the ensemble of structures present for the polysaccharides in solution were consistent with the results from their ¹H,¹H-NOESY experiments. In addition, the similarities between the O-antigen on its own and as a constituent of the full LPS in bilayer environment makes it possible to realistically describe the LPS conformation and dynamics from the MD simulations.

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Uniform and non-uniform sampled NOESY NMR data yield similar internuclear distances.

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O-antigen internuclear distances from NMR and MD show excellent agreement.

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O-antigen ensemble structures from MD are consistent with NMR observations.

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O-antigen structures are more extended in LPS bilayers than in solution.

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MD simulations can describe realistic LPS conformation and dynamics.

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof-of-concept of the application of deep learning and neural networks for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.

Codon Harmonization of a Kir3.1-KirBac1.3 Chimera for Structural Study Optimization

The expression of functional, folded, and isotopically enriched membrane proteins is an enduring bottleneck for nuclear magnetic resonance (NMR) studies. Indeed, historically, protein yield optimization has been insufficient to allow NMR analysis of many complex Eukaryotic membrane proteins. However, recent work has found that manipulation of plasmid codons improves the odds of successful NMR-friendly protein production. In the last decade, numerous studies showed that matching codon usage patterns in recombinant gene sequences to those in the native sequence is positively correlated with increased protein yield. This phenomenon, dubbed codon harmonization, may be a powerful tool in optimizing recombinant expression of difficult-to-produce membrane proteins for structural studies. Here, we apply this technique to an inward rectifier K⁺ Channel (Kir) 3.1-KirBac1.3 chimera. Kir3.1 falls within the G protein-coupled inward rectifier K⁺ (GIRK) channel family, thus NMR studies may inform on the nuances of GIRK gating action in the presence and absence of its G Protein, lipid, and small molecule ligands. In our hands, harmonized plasmids increase protein yield nearly two-fold compared to the traditional ‘fully codon optimized’ construct. We then employ a fluorescence-based functional assay and solid-state NMR correlation spectroscopy to show the final protein product is folded and functional.

Enhancing Compression Level for More Efficient Compressed Sensing and Other Lessons from NMR Spectroscopy

Modern nuclear magnetic resonance spectroscopy (NMR) is based on two- and higher-dimensional experiments that allow the solving of molecular structures, i.e., determine the relative positions of single atoms very precisely. However, rich chemical information comes at the price of long data acquisition times (up to several days). This problem can be alleviated by compressed sensing (CS)—a method that revolutionized many fields of technology. It is known that CS performs the most efficiently when measured objects feature a high level of compressibility, which in the case of NMR signal means that its frequency domain representation (spectrum) has a low number of significant points. However, many NMR spectroscopists are not aware of the fact that various well-known signal acquisition procedures enhance compressibility and thus should be used prior to CS reconstruction. In this study, we discuss such procedures and show to what extent they are complementary to CS approaches. We believe that the survey will be useful not only for NMR spectroscopists but also to inspire the broader signal processing community.

Fast time-resolved NMR with non-uniform sampling

NMR spectroscopy is a versatile tool for studying time-dependent processes: chemical reactions, phase transitions or macromolecular structure changes. However, time-resolved NMR is usually based on the simplest among available techniques - one-dimensional spectra serving as "snapshots" of the studied process. One of the reasons is that multidimensional experiments are very time-expensive due to costly sampling of evolution time space. In this review we summarize efforts to alleviate the problem of limited applicability of multidimensional NMR in time-resolved studies. We focus on techniques based on sparse or non-uniform sampling (NUS), which lead to experimental time reduction by omitting a significant part of the data during measurement and reconstructing it mathematically, adopting certain assumptions about the spectrum. NUS spectra are faster to acquire than conventional ones and thus better suited to the role of "snapshots", but still suffer from non-stationarity of the signal i.e. amplitude and frequency variations within a dataset. We discuss in detail how these instabilities affect the spectra, and what are the optimal ways of sampling the non-stationary FID signal. Finally, we discuss related areas of NMR where serial experiments are exploited and how they can benefit from the same NUS-based approaches.

A suite of solid-state NMR experiments to utilize orphaned magnetization for assignment of proteins using parallel high and low gamma detection

We present a suite of two-receiver solid-state NMR experiments for backbone and side chain resonance assignment. The experiments rely on either dipolar coupling or scalar coupling for polarization transfer and are devised to acquire a ¹H-detected 3D experiment AND a nested ¹³C-detected 2D from a shared excitation pulse. In order to compensate for the lower sensitivity of detection on ¹³C nucleus, 2D rows are signal averaged during 3D planes. The 3D dual receiver experiments do not suffer from any appreciable signal loss compared to their single receiver versions and require no extra optimization. The resulting data is higher in information content with no additional experiment time. The approach is expected to become widespread as multiple receivers become standard for new NMR spectrometers.

1H, 15N, and 13C resonance assignments of the intrinsically disordered SH4 and Unique domains of Hck

Hematopoietic cell kinase (Hck) is an important signaling enzyme and a potential drug target for HIV infections and Bcr/Abl-chronic myeloid leukemia. The protein shares the same SH4-Unique-SH3-SH2-kinase multi-domain architecture as the other eight members of the Src family of non-receptor tyrosine kinases. These enzymes are often found anchored to the intracellular side of the membrane via lipidation of the SH4 domain and are integral components of signaling cascades localized at the cell surface. Despite the detailed structural information available for the SH3, SH2, and kinase domains of Hck, the intrinsically disordered nature of the SH4 and Unique domains has resulted in a lack of information for this important region of the protein that is responsible for membrane association. Here, we report the ¹H, ¹⁵N and ¹³C chemical shifts of the Hck SH4-Unique domains at pH 4.5.

The Human Fecal Microbiota Metabolizes Foodborne Heterocyclic Aromatic Amines by Reuterin Conjugation and Further Transformations

SCOPE

Heterocyclic aromatic amines (HAAs) are process-induced food contaminants with high mutagenic and/or carcinogenic potential. Although the human gut microbiota is known to affect the metabolism of dietary constituents, its impact on HAA metabolism and toxicity has been little studied. Here, the glycerol-dependent metabolism of seven foodborne HAAs (AαC, Trp-P-1, harman, norharman, PhIP, MeIQx, and MeIQ) by the human fecal microbiota is investigated.

METHODS AND RESULTS

As analyzed by HPLC-DAD/FLD, the extent of conversion is strongly dependent on glycerol supplementation and HAA structure. AαC (60-100%) and the 2-aminoimidazoazarenes (up to 58%) are especially prone to microbial conversion. Based on high-resolution MS and/or NMR spectroscopy data, 70 fecal metabolites are identified in total, mainly formed by chemical reactions with one or two molecules of microbially derived reuterin. Moreover, it has been demonstrated that the human fecal microbiota can further transform reuterin adducts by reduction and/or hydroxylation reactions. Upon isolation, some reuterin-induced HAA metabolites appear to be partially unstable, complicating structural identification.

CONCLUSION

The formation of microbial metabolites needs to be incorporated into risk assessment considerations for HAAs in human health. In this study, several HAA metabolites, mainly reuterin-dependent, are identified in vitro, providing the basis for future human studies investigating microbial HAA metabolism.

XLSY: Extra-Large NMR Spectroscopy

NMR studies of intrinsically disordered proteins and other complex biomolecular systems require spectra with the highest resolution and dimensionality. An efficient approach, extra‐large NMR spectroscopy, is presented for experimental data collection, reconstruction, and handling of very large NMR spectra by a combination of the radial and non‐uniform sampling, a new processing algorithm, and rigorous statistical validation. We demonstrate the first high‐quality reconstruction of a full seven‐dimensional HNCOCACONH and two five‐dimensional HACACONH and HN(CA)CONH experiments for a representative intrinsically disordered protein α‐synuclein. XLSY will significantly enhance the NMR toolbox in challenging biomolecular studies.

Systematic Evaluation of Non-Uniform Sampling Parameters in the Targeted Analysis of Urine Metabolites by 1H,1H 2D NMR Spectroscopy

Non-uniform sampling (NUS) allows the accelerated acquisition of multidimensional NMR spectra. The aim of this contribution was the systematic evaluation of the impact of various quantitative NUS parameters on the accuracy and precision of 2D NMR measurements of urinary metabolites. Urine aliquots spiked with varying concentrations (15.6-500.0 µM) of tryptophan, tyrosine, glutamine, glutamic acid, lactic acid, and threonine, which can only be resolved fully by 2D NMR, were used to assess the influence of the sampling scheme, reconstruction algorithm, amount of omitted data points, and seed value on the quantitative performance of NUS in 1H,1H-TOCSY and 1H,1H-COSY45 NMR spectroscopy. Sinusoidal Poisson-gap sampling and a compressed sensing approach employing the iterative re-weighted least squares method for spectral reconstruction allowed a 50% reduction in measurement time while maintaining sufficient quantitative accuracy and precision for both types of homonuclear 2D NMR spectroscopy. Together with other advances in instrument design, such as state-of-the-art cryogenic probes, use of 2D NMR spectroscopy in large biomedical cohort studies seems feasible.

High-fidelity spectroscopy reconstruction in accelerated NMR

High-fidelity spectra, particularly low intensity peaks, are reconstructed for fast NMR with better rank approximation in the EnhanCed Low Rank (ECLR) method.

Revealing true coupling strengths in two-dimensional spectroscopy with sparsity-based signal recovery

Two-dimensional (2D) spectroscopy is used to study the interactions between energy levels in both the field of optics and nuclear magnetic resonance (NMR). Conventionally, the strength of interaction between two levels is inferred from the value of their common off-diagonal peak in the 2D spectrum, which is termed the cross peak. However, stronger diagonal peaks often have long tails that extend into the locations of the cross peaks and alter their values. Here, we introduce a method for retrieving the true interaction strengths by using sparse signal recovery techniques and apply our method in 2D Raman spectroscopy experiments.

Measurement of protein backbone 13CO and 15N relaxation dispersion at high resolution

Peak overlap in crowded regions of two-dimensional spectra prevents characterization of dynamics for many sites of interest in globular and intrinsically disordered proteins. We present new three-dimensional pulse sequences for measurement of Carr-Purcell-Meiboom-Gill relaxation dispersions at backbone nitrogen and carbonyl positions. To alleviate increase in the measurement time associated with the additional spectral dimension, we use non-uniform sampling in combination with two distinct methods of spectrum reconstruction: compressed sensing and co-processing with multi-dimensional decomposition. The new methodology was validated using disordered protein CD79A from B-cell receptor and an SH3 domain from Abp1p in exchange between its free form and bound to a peptide from the protein Ark1p. We show that, while providing much better resolution, the 3D NUS experiments give the similar accuracy and precision of the dynamic parameters to ones obtained using traditional 2D experiments. Furthermore, we show that jackknife resampling of the spectra yields robust estimates of peak intensities errors, eliminating the need for recording duplicate data points.

Solution NMR views of dynamical ordering of biomacromolecules

BACKGROUND

To understand the mechanisms related to the 'dynamical ordering' of macromolecules and biological systems, it is crucial to monitor, in detail, molecular interactions and their dynamics across multiple timescales. Solution nuclear magnetic resonance (NMR) spectroscopy is an ideal tool that can investigate biophysical events at the atomic level, in near-physiological buffer solutions, or even inside cells.

SCOPE OF REVIEW

In the past several decades, progress in solution NMR has significantly contributed to the elucidation of three-dimensional structures, the understanding of conformational motions, and the underlying thermodynamic and kinetic properties of biomacromolecules. This review discusses recent methodological development of NMR, their applications and some of the remaining challenges.

MAJOR CONCLUSIONS

Although a major drawback of NMR is its difficulty in studying the dynamical ordering of larger biomolecular systems, current technologies have achieved considerable success in the structural analysis of substantially large proteins and biomolecular complexes over 1MDa and have characterised a wide range of timescales across which biomolecular motion exists. While NMR is well suited to obtain local structure information in detail, it contributes valuable and unique information within hybrid approaches that combine complementary methodologies, including solution scattering and microscopic techniques.

GENERAL SIGNIFICANCE

For living systems, the dynamic assembly and disassembly of macromolecular complexes is of utmost importance for cellular homeostasis and, if dysregulated, implied in human disease. It is thus instructive for the advancement of the study of the dynamical ordering to discuss the potential possibilities of solution NMR spectroscopy and its applications. This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato.

Optimized fast mixing device for real-time NMR applications

We present an improved fast mixing device based on the rapid mixing of two solutions inside the NMR probe, as originally proposed by Hore and coworkers (J. Am. Chem. Soc. 125 (2003) 12484-12492). Such a device is important for off-equilibrium studies of molecular kinetics by multidimensional real-time NMR spectrsocopy. The novelty of this device is that it allows removing the injector from the NMR detection volume after mixing, and thus provides good magnetic field homogeneity independently of the initial sample volume placed in the NMR probe. The apparatus is simple to build, inexpensive, and can be used without any hardware modification on any type of liquid-state NMR spectrometer. We demonstrate the performance of our fast mixing device in terms of improved magnetic field homogeneity, and show an application to the study of protein folding and the structural characterization of transiently populated folding intermediates.

Acceleration of protein backbone NMR assignment by combinatorial labeling: Application to a small molecule binding study

Selective labeling with stable isotopes has long been recognized as a valuable tool in protein NMR to alleviate signal overlap and sensitivity limitations. In this study, combinatorial 15 N-, 13 Cα -, and 13 C'-selective labeling has been used during the backbone assignment of human cyclophilin D to explore binding of an inhibitor molecule. Using a cell-free expression system, a scheme that involves 15 N, 1-13 C, 2-13 C, fully 15 N/13 C, and unlabeled amino acids was optimized to gain a maximum of assignment information from three samples. This scheme was combined with time-shared triple-resonance NMR experiments, which allows a fast and efficient backbone assignment by giving the unambiguous assignment of unique amino acid pairs in the protein, the identity of ambiguous pairs and information about all 19 non-proline amino acid types. It is therefore well suited for binding studies where de novo assignments of amide 1 H and 15 N resonances need to be obtained, even in cases where sensitivity is the limiting factor.

Accelerating two-dimensional nuclear magnetic resonance correlation spectroscopy via selective coherence transfer

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool for both qualitative and quantitative analyses of various systems in chemistry, biology, and medicine. However, applications of one-dimensional ¹H NMR are often restrained by the presence of severe overlap among different resonances. The advent of two-dimensional (2D) ¹H NMR constitutes a promising alternative by extending the crowded resonances into a plane and thereby alleviating the spectral congestions. However, the enhanced ability in discriminating resonances is achieved at the cost of extended experimental duration due to necessity of various scans with progressive delays to construct the indirect dimension. Therefore, in this study, we propose a selective coherence transfer (SECOT) method to accelerate acquisitions of 2D correlation spectroscopy by converting chemical shifts into spatial positions within the effective sample length and then performing an echo planar spectroscopic imaging module to record the spatial and spectral information, which generates 2D correlation spectrum after 2D Fourier transformation. The feasibility and effectiveness of SECOT have been verified by a set of experiments under both homogeneous and inhomogeneous magnetic fields. Moreover, evaluations of SECOT for quantitative analyses are carried out on samples with a series of different concentrations. Based on these experimental results, the SECOT may open important perspectives for fast, accurate, and stable investigations of various chemical systems both qualitatively and quantitatively.

Probing conformational dynamics in biomolecules via chemical exchange saturation transfer: a primer

Although Chemical Exchange Saturation Transfer (CEST) type NMR experiments have been used to study chemical exchange processes in molecules since the early 1960s, there has been renewed interest in the past several years in using this approach to study biomolecular conformational dynamics. The methodology is particularly powerful for the study of sparsely populated, transiently formed conformers that are recalcitrant to investigation using traditional biophysical tools, and it is complementary to relaxation dispersion and magnetization transfer experiments that have traditionally been used to study chemical exchange processes. Here we discuss the concepts behind the CEST experiment, focusing on practical aspects as well, we review some of the pulse sequences that have been developed to characterize protein and RNA conformational dynamics, and we discuss a number of examples where the CEST methodology has provided important insights into the role of dynamics in biomolecular function.

Dodecylphosphocholine Micelles Induce Amyloid Formation of the PrP(110-136) Peptide via an α-Helical Metastable Conformation

A peptide encompassing the conserved hydrophobic region and the first β-strand of the prion protein (PrP(110-136)) shown to interact with the surface of dodecylphosphocholine micelles adopts an α-helical conformation that is localized below the head-group layer. This surface-bound peptide has a half-life of one day, and readily initiates the formation of amyloid fibrils. The presence of the latter was confirmed using birefringence microscopy upon Congo red binding and thioflavin T-binding induced fluorescence. The observation of this metastable α-helical conformer provides a unique snapshot of the early steps of the inter-conversion pathway. These findings together with the body of evidence from the prion literature allowed us to propose a mechanism for the conversion of PrPC to amyloid material.