Structure determination using solution NMR: Is it worth the effort?

In-Cell 19F NMR of Proteins: Recent Progress and Future Opportunities

In vitro, 19F NMR methodology is preferably selected as a complementary and straightforward method for unveiling the conformations, dynamics, and interactions of biological molecules. Its effectiveness in vivo has seen continuous improvement, addressing challenges faced by conventional heteronuclear NMR experiments on structured proteins, such as severe line broadening, low signal-to-noise ratio, and background signals. Herein, we summarize the distinctive advantages of 19F NMR, along with recent progress in sample preparation and applications within the realm of in-cell NMR. Additionally, we offer insights into the future directions and prospects of this methodology based on our understanding.

The Complementarity of Nuclear Magnetic Resonance and Native Mass Spectrometry in Probing Protein-Protein Interactions

Nuclear magnetic resonance (NMR) and native mass spectrometry (MS) are mature physicochemical techniques with long histories and important applications. NMR spectroscopy provides detailed information about the structure, dynamics, interactions, and chemical environment of biomolecules. MS is an effective approach for determining the mass of biomolecules with high accuracy, sensitivity, and speed. The two techniques offer unique advantages and provide solid tools for structural biology. In the present review, we discuss their individual merits in the context of their applications to structural studies in biology with specific focus on protein interactions and evaluate their limitations. We provide specific examples in which these techniques can complement each other, providing new information on the same scientific case. We discuss how the field may develop and what challenges are expected in the future. Overall, the combination of NMR and MS plays an increasingly important role in integrative structural biology, assisting scientists in deciphering the three-dimensional structure of composite macromolecular assemblies.

Application of vibrational spectroscopy and nuclear magnetic resonance methods for drugs pharmacokinetics research

OBJECTIVES

The development of new methods for determining the concentration of drugs is an actual topic today. The article contains a detailed review on vibrational spectroscopy and nuclear magnetic resonance methods using for pharmacokinetic research. This study is devoted to the possibility of using vibrational spectroscopy and 1H nuclear magnetic resonance spectroscopy to determine the concentration of drugs and the use of these groups of techniques for therapeutic drug monitoring.

CONTENT

The study was conducted by using scientific libraries (Scopus, Web of Science Core Collection, Medline, GoogleScholar, eLIBRARY, PubMed) and reference literature. A search was conducted for the period from 2011 to 2021 in Russian and English, by combinations of words: 1H nuclear magnetic resonance (¹H NMR), vibrational spectroscopy, Surface-Enhanced Raman spectroscopy, drug concentration, therapeutic drug monitoring. These methods have a number of advantages and are devoid of some of the disadvantages of classical therapeutic drug monitoring (TDM) methods - high performance liquid chromatography and mass spectrometry. This review considers the possibility of using the methods of surface-enhanced Raman scattering (SERS) and ¹H NMR-spectroscopy to assess the concentration of drugs in various biological media (blood, urine), as well as to study intracellular metabolism and the metabolism of ophthalmic drugs. ¹Н NMR-spectroscopy can be chosen as a TDM method, since it allows analyzing the structure and identifying metabolites of various drugs. ¹Н NMR-based metabolomics can provide information on the side effects of drugs, predict response to treatment, and provide key information on the mechanisms of action of known and new drug compounds.

SUMMARY AND OUTLOOK

SERS and ¹Н NMR-spectroscopy have great potential for further study and the possibility of introducing them into clinical practice, including for evaluating the efficacy and safety of drugs.

In-cell NMR: Why and how?

NMR spectroscopy has been applied to cells and tissues analysis since its beginnings, as early as 1950. We have attempted to gather here in a didactic fashion the broad diversity of data and ideas that emerged from NMR investigations on living cells. Covering a large proportion of the periodic table, NMR spectroscopy permits scrutiny of a great variety of atomic nuclei in all living organisms non-invasively. It has thus provided quantitative information on cellular atoms and their chemical environment, dynamics, or interactions. We will show that NMR studies have generated valuable knowledge on a vast array of cellular molecules and events, from water, salts, metabolites, cell walls, proteins, nucleic acids, drugs and drug targets, to pH, redox equilibria and chemical reactions. The characterization of such a multitude of objects at the atomic scale has thus shaped our mental representation of cellular life at multiple levels, together with major techniques like mass-spectrometry or microscopies. NMR studies on cells has accompanied the developments of MRI and metabolomics, and various subfields have flourished, coined with appealing names: fluxomics, foodomics, MRI and MRS (i.e. imaging and localized spectroscopy of living tissues, respectively), whole-cell NMR, on-cell ligand-based NMR, systems NMR, cellular structural biology, in-cell NMR… All these have not grown separately, but rather by reinforcing each other like a braided trunk. Hence, we try here to provide an analytical account of a large ensemble of intricately linked approaches, whose integration has been and will be key to their success. We present extensive overviews, firstly on the various types of information provided by NMR in a cellular environment (the "why", oriented towards a broad readership), and secondly on the employed NMR techniques and setups (the "how", where we discuss the past, current and future methods). Each subsection is constructed as a historical anthology, showing how the intrinsic properties of NMR spectroscopy and its developments structured the accessible knowledge on cellular phenomena. Using this systematic approach, we sought i) to make this review accessible to the broadest audience and ii) to highlight some early techniques that may find renewed interest. Finally, we present a brief discussion on what may be potential and desirable developments in the context of integrative studies in biology.

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Merging NMR Data and Computation Facilitates Data-Centered Research

The Biological Magnetic Resonance Data Bank (BMRB) has served the NMR structural biology community for 40 years, and has been instrumental in the development of many widely-used tools. It fosters the reuse of data resources in structural biology by embodying the FAIR data principles (Findable, Accessible, Inter-operable, and Re-usable). NMRbox is less than a decade old, but complements BMRB by providing NMR software and high-performance computing resources, facilitating the reuse of software resources. BMRB and NMRbox both facilitate reproducible research. NMRbox also fosters the development and deployment of complex meta-software. Combining BMRB and NMRbox helps speed and simplify workflows that utilize BMRB, and enables facile federation of BMRB with other data repositories. Utilization of BMRB and NMRbox in tandem will enable additional advances, such as machine learning, that are poised to become increasingly powerful.

Reconstruction of Coupled Intra- and Interdomain Protein Motion from Nuclear and Electron Magnetic Resonance

Proteins composed of multiple domains allow for structural heterogeneity and interdomain dynamics that may be vital for function. Intradomain structures and dynamics can influence interdomain conformations and vice versa. However, no established structure determination method is currently available that can probe the coupling of these motions. The protein Pin1 contains separate regulatory and catalytic domains that sample "extended" and "compact" states, and ligand binding changes this equilibrium. Ligand binding and interdomain distance have been shown to impact the activity of Pin1, suggesting interdomain allostery. In order to characterize the conformational equilibrium of Pin1, we describe a novel method to model the coupling between intra- and interdomain dynamics at atomic resolution using multistate ensembles. The method uses time-averaged nuclear magnetic resonance (NMR) restraints and double electron-electron resonance (DEER) data that resolve distance distributions. While the intradomain calculation is primarily driven by exact nuclear Overhauser enhancements (eNOEs), J couplings, and residual dipolar couplings (RDCs), the relative domain distribution is driven by paramagnetic relaxation enhancement (PREs), RDCs, interdomain NOEs, and DEER. Our data support a 70:30 population of the compact and extended states in apo Pin1. A multistate ensemble describes these conformations simultaneously, with distinct conformational differences located in the interdomain interface stabilizing the compact or extended states. We also describe correlated conformations between the catalytic site and interdomain interface that may explain allostery driven by interdomain contact.

NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview

BACKGROUND

NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems.

OBJECTIVE

This review provides a toolkit to investigate peptide conformational properties by NMR.

METHODS

Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well.

RESULTS

The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well.

CONCLUSION

NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand interactions with diverse binding affinities. NMR spectroscopy is able to identify fragments that bind weakly to a target, making it valuable for identifying hits targeting undruggable sites. In this review, we summarize the roles of solution NMR spectroscopy in drug discovery. We describe some methods that are used in identifying fragments, understanding the mechanism of action for a ligand, and monitoring the conformational changes of a target induced by ligand binding. A number of studies have proven that ¹⁹F-NMR is very powerful in screening fragments and detecting protein conformational changes. In-cell NMR will also play important roles in drug discovery by elucidating protein-ligand interactions in living cells.

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix Factorization

Nuclear magnetic resonance (NMR) spectroscopy is commonly used to characterize molecular complexity because it produces informative atomic-resolution data on the chemical structure and molecular mobility of samples non-invasively by means of various acquisition parameters and pulse programs. However, analyzing the accumulated NMR data of mixtures is challenging due to noise and signal overlap. Therefore, data-cleansing steps, such as quality checking, noise reduction, and signal deconvolution, are important processes before spectrum analysis. Here, we have developed an NMR measurement informatics tool for data cleansing that combines short-time Fourier transform (STFT; a time-frequency analytical method) and probabilistic sparse matrix factorization (PSMF) for signal deconvolution and noise factor analysis. Our tool can be applied to the original free induction decay (FID) signals of a one-dimensional NMR spectrum. We show that the signal deconvolution method reduces the noise of FID signals, increasing the signal-to-noise ratio (SNR) about tenfold, and its application to diffusion-edited spectra allows signals of macromolecules and unsuppressed small molecules to be separated by the length of the T₂* relaxation time. Noise factor analysis of NMR datasets identified correlations between SNR and acquisition parameters, identifying major experimental factors that can lower SNR.

Insights into Structures and Dynamics of Flavivirus Proteases from NMR Studies

Nuclear magnetic resonance (NMR) spectroscopy plays important roles in structural biology and drug discovery, as it is a powerful tool to understand protein structures, dynamics, and ligand binding under physiological conditions. The protease of flaviviruses is an attractive target for developing antivirals because it is essential for the maturation of viral proteins. High-resolution structures of the proteases in the absence and presence of ligands/inhibitors were determined using X-ray crystallography, providing structural information for rational drug design. Structural studies suggest that proteases from Dengue virus (DENV), West Nile virus (WNV), and Zika virus (ZIKV) exist in open and closed conformations. Solution NMR studies showed that the closed conformation is predominant in solution and should be utilized in structure-based drug design. Here, we reviewed solution NMR studies of the proteases from these viruses. The accumulated studies demonstrated that NMR spectroscopy provides additional information to understand conformational changes of these proteases in the absence and presence of substrates/inhibitors. In addition, NMR spectroscopy can be used for identifying fragment hits that can be further developed into potent protease inhibitors.