Unambiguous Tracking of Protein Phosphorylation by Fast High-Resolution FOSY NMR*.

Dysregulation of post-translational modifications (PTMs) like phosphorylation is often involved in disease. NMR may elucidate exact loci and time courses of PTMs at atomic resolution and near-physiological conditions but requires signal assignment to individual atoms. Conventional NMR methods for this base on tedious global signal assignment that may often fail, as for large intrinsically disordered proteins (IDPs). We present a sensitive, robust alternative to rapidly obtain only the local assignment near affected signals, based on FOcused SpectroscopY (FOSY) experiments using selective polarisation transfer (SPT). We prove its efficiency by identifying two phosphorylation sites of glycogen synthase kinase 3 beta (GSK3ß) in human Tau40, an IDP of 441 residues, where the extreme spectral dispersion in FOSY revealed unprimed phosphorylation also of Ser409. FOSY may broadly benefit NMR studies of PTMs and other hotspots in IDPs, including sites involved in molecular interactions.

Modulation of Structural Heterogeneity Controls Phytochrome Photoswitching.

Phytochromes sense red/far-red light and control many biological processes in plants, fungi, and bacteria. Although the crystal structures of dark- and light-adapted states have been determined, the molecular mechanisms underlying photoactivation remain elusive. Here, we demonstrate that the conserved tongue region of the PHY domain of a 57-kDa photosensory module of Deinococcus radiodurans phytochrome changes from a structurally heterogeneous dark state to an ordered, light-activated state. The results were obtained in solution by utilizing a laser-triggered activation approach detected on the atomic level with high-resolution protein NMR spectroscopy. The data suggest that photosignaling of phytochromes relies on careful modulation of structural heterogeneity of the PHY tongue.

Rapid Biophysical Characterization and NMR Spectroscopy Structural Analysis of Small Proteins from Bacteria and Archaea.

Proteins encoded by small open reading frames (sORFs) have a widespread occurrence in diverse microorganisms and can be of high functional importance. However, due to annotation biases and their technically challenging direct detection, these small proteins have been overlooked for a long time and were only recently rediscovered. The currently rapidly growing number of such proteins requires efficient methods to investigate their structure-function relationship. Herein, a method is presented for fast determination of the conformational properties of small proteins. Their small size makes them perfectly amenable for solution-state NMR spectroscopy. NMR spectroscopy can provide detailed information about their conformational states (folded, partially folded, and unstructured). In the context of the priority program on small proteins funded by the German research foundation (SPP2002), 27 small proteins from 9 different bacterial and archaeal organisms have been investigated. It is found that most of these small proteins are unstructured or partially folded. Bioinformatics tools predict that some of these unstructured proteins can potentially fold upon complex formation. A protocol for fast NMR spectroscopy structure elucidation is described for the small proteins that adopt a persistently folded structure by implementation of new NMR technologies, including automated resonance assignment and nonuniform sampling in combination with targeted acquisition.

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.

Evaluation of Discrimination Performance in Case for Multiple Non-Discriminated Samples: Classification of Honeys by Fluorescent Fingerprinting.

In this study we develop a variant of fluorescent sensor array technique based on addition of fluorophores to samples. A correct choice of fluorophores is critical for the successful application of the technique, which calls for the necessity of comparing different discrimination protocols. We used 36 honey samples from different sources to which various fluorophores were added (tris-(2,2'-bipyridyl) dichlororuthenium(II) (Ru(bpy)32+), zinc(II) 8-hydroxyquinoline-5-sulfonate (8-Ox-Zn), and thiazole orange in the presence of two types of deoxyribonucleic acid). The fluorescence spectra were obtained within 400-600 nm and treated by principal component analysis (PCA). No fluorophore allowed for the discrimination of all samples. To evaluate the discrimination performance of fluorophores, we introduced crossing number (CrN) calculated as the number of mutual intersections of confidence ellipses in the PCA scores plots, and relative position (RP) characterized by the pairwise mutual location of group centers and their most distant points. CrN and RP parameters correlated with each other, with total sensitivity (TS) calculated by Mahalanobis distances, and with the overall rating based on all metrics, with coefficients of correlation over 0.7. Most of the considered parameters gave the first place in the discrimination performance to Ru(bpy)32+ fluorophore.

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.

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.

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.

A Population Shift between Sparsely Populated Folding Intermediates Determines Amyloidogenicity.

The balance between protein folding and misfolding is a crucial determinant of amyloid assembly. Transient intermediates that are sparsely populated during protein folding have been identified as key players in amyloid aggregation. However, due to their ephemeral nature, structural characterization of these species remains challenging. Here, using the power of nonuniformly sampled NMR methods we investigate the folding pathway of amyloidogenic and nonamyloidogenic variants of ß2-microglobulin (ß2m) in atomic detail. Despite folding via common intermediate states, we show that the decreased population of the aggregation-prone ITrans state and population of a less stable, more dynamic species ablate amyloid formation by increasing the energy barrier for amyloid assembly. The results show that subtle changes in conformational dynamics can have a dramatic effect in determining whether a protein is amyloidogenic, without perturbation of the mechanism of protein folding.

Fast multi-dimensional NMR acquisition and processing using the sparse FFT.

Increasing the dimensionality of NMR experiments strongly enhances the spectral resolution and provides invaluable direct information about atomic interactions. However, the price tag is high: long measurement times and heavy requirements on the computation power and data storage. We introduce sparse fast Fourier transform as a new method of NMR signal collection and processing, which is capable of reconstructing high quality spectra of large size and dimensionality with short measurement times, faster computations than the fast Fourier transform, and minimal storage for processing and handling of sparse spectra. The new algorithm is described and demonstrated for a 4D BEST-HNCOCA spectrum.

Non-uniform sampling: post-Fourier era of NMR data collection and processing.

The invention of multidimensional techniques in the 1970s revolutionized NMR, making it the general tool of structural analysis of molecules and materials. In the most straightforward approach, the signal sampling in the indirect dimensions of a multidimensional experiment is performed in the same manner as in the direct dimension, i.e. with a grid of equally spaced points. This results in lengthy experiments with a resolution often far from optimum. To circumvent this problem, numerous sparse-sampling techniques have been developed in the last three decades, including two traditionally distinct approaches: the radial sampling and non-uniform sampling. This mini review discusses the sparse signal sampling and reconstruction techniques from the point of view of an underdetermined linear algebra problem that arises when a full, equally spaced set of sampled points is replaced with sparse sampling. Additional assumptions that are introduced to solve the problem, as well as the shape of the undersampled Fourier transform operator (visualized as so-called point spread function), are shown to be the main differences between various sparse-sampling methods.

Accelerated NMR spectroscopy with low-rank reconstruction.

Accelerated multi-dimensional NMR spectroscopy is a prerequisite for high-throughput applications, studying short-lived molecular systems and monitoring chemical reactions in real time. Non-uniform sampling is a common approach to reduce the measurement time. Here, a new method for high-quality spectra reconstruction from non-uniformly sampled data is introduced, which is based on recent developments in the field of signal processing theory and uses the so far unexploited general property of the NMR signal, its low rank. Using experimental and simulated data, we demonstrate that the low-rank reconstruction is a viable alternative to the current state-of-the-art technique compressed sensing. In particular, the low-rank approach is good in preserving of low-intensity broad peaks, and thus increases the effective sensitivity in the reconstructed spectra.

Time-resolved multidimensional NMR with non-uniform sampling.

Time-resolved experiments demand high resolution both in spectral dimensions and in time of the studied kinetic process. The latter requirement traditionally prohibits applications of the multidimensional experiments, which, although capable of providing invaluable information about structure and dynamics and almost unlimited spectral resolution, require too lengthy data collection. Our work shows that the problem has a solution in using modern methods of NMR data collection and signal processing. A continuous fast pulsing three-dimensional experiment is acquired using non-uniform sampling during full time of the studied reaction. High sensitivity and time-resolution of a few minutes is achieved by simultaneous processing of the full data set with the multi-dimensional decomposition. The method is verified and illustrated in realistic simulations and by measuring deuterium exchange rates of amide protons in ubiquitin. We applied the method for characterizing kinetics of in vitro phosphorylation of two tyrosine residues in an intrinsically disordered cytosolic domain of the B cell receptor protein CD79b. Signals of many residues including tyrosines in both phosphorylated and unmodified forms of CD79b are found in a heavily crowded region of 2D ¹H-¹5N correlation spectrum and the significantly enhanced spectral resolution provided by the 3D time-resolved approach was essential for the quantitative site-specific analysis.

Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses.

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR 15N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

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.

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.

Combined NMR and molecular dynamics conformational filter identifies unambiguously dynamic ensembles of Dengue protease NS2B/NS3pro.

The dengue protease NS2B/NS3pro has been reported to adopt either an 'open' or a 'closed' conformation. We have developed a conformational filter that combines NMR with MD simulations to identify conformational ensembles that dominate in solution. Experimental values derived from relaxation parameters for the backbone and methyl side chains were compared with the corresponding back-calculated relaxation parameters of different conformational ensembles obtained from free MD simulations. Our results demonstrate a high prevalence for the 'closed' conformational ensemble while the 'open' conformation is absent, indicating that the latter conformation is most probably due to crystal contacts. Conversely, conformational ensembles in which the positioning of the co-factor NS2B results in a 'partially' open conformation, previously described in both MD simulations and X-ray studies, were identified by our conformational filter. Altogether, we believe that our approach allows for unambiguous identification of true conformational ensembles, an essential step for reliable drug discovery.

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.

Resolution enhancement of NMR by decoupling with the low-rank Hankel model.

Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine. Although J-coupling carries essential structural information it may also limit the spectral resolution. Homonuclear decoupling remains a challenging problem. In this work, we introduce a new approach that uses a specific coupling value as prior knowledge, and the Hankel property of the exponential NMR signal to achieve broadband heteronuclear decoupling using the low-rank method. Our results on synthetic and realistic HMQC spectra demonstrate that the proposed method not only effectively enhances resolution by decoupling, but also maintains sensitivity and suppresses spectral artefacts. The approach can be combined with non-uniform sampling, which means that the resolution can be further improved without any extra acquisition time.

1H, 13C and 15N resonance assignment of backbone and IVL-methyl side chain of the S135A mutant NS3pro/NS2B protein of Dengue II virus reveals unique secondary structure features in solution.

The serotype II Dengue (DENV 2) virus is the most prevalent of all four known serotypes. Herein, we present nearly complete 1H, 15N, and 13C backbone and 1H, 13C isoleucine, valine, and leucine methyl resonance assignment of the apo S135A catalytically inactive variant of the DENV 2 protease enzyme folded as a tandem formed between the serine protease domain NS3pro and the cofactor NS2B, as well as the secondary structure prediction of this complex based on the assigned chemical shifts using the TALOS-N software. Our results provide a solid ground for future elucidation of the structure and dynamic of the apo NS3pro/NS2B complex, key for adequate development of inhibitors, and a thorough molecular understanding of their function(s).