Perspectives on NMR in drug discovery: a technique comes of age

Exploring a Potential Optimization Route for Peptide Ligands of the Sam Domain from the Lipid Phosphatase Ship2

The Sam (Sterile alpha motif) domain of the lipid phosphatase Ship2 (Ship2-Sam) is engaged by the Sam domain of the receptor tyrosine kinase EphA2 (EphA2-Sam) and, this interaction is principally linked to procancer effects. Peptides able to hinder the formation of the EphA2-Sam/Ship2-Sam complex could possess therapeutic potential. Herein, by employing the FoldX software suite, we set up an in silico approach to improve the peptide targeting of the so-called Mid Loop interface of Ship2-Sam, representing the EphA2-Sam binding site. Starting from a formerly identified peptide antagonist of the EphA2-Sam/Ship2-Sam association, first, the most stabilizing mutations that could be inserted in each peptide position were predicted. Then, they were combined, producing a list of potentially enhanced Ship2-Sam ligands. A few of the in silico generated peptides were experimentally evaluated. Interaction assays with Ship2-Sam were performed using NMR and BLI (BioLayer Interferometry). In vitro assays were conducted as well to check for cytotoxic effects against both cancerous and healthy cells, and also to assess the capacity to regulate EphA2 degradation. This study undoubtedly enlarges our knowledge on how to properly target EphA2-Sam/Ship2-Sam associations with peptide-based tools and provides a promising strategy that can be used to target any protein-protein interaction.

Inhibition of the EphA2-Sam/Ship2-Sam Association through Peptide Ligands: Studying the Combined Effect of Charge and Aromatic Character

The Sam (sterile alpha motif) domain from the lipid phosphatase Ship2 binds the Sam domain from the EphA2 receptor to negatively regulate receptor endocytosis and degradation. This interaction is primarily linked to pro-oncogenic effects. We report on the design and evaluation of EphA2-Sam/Ship2-Sam peptide inhibitors provided with positive charges and different aromatic characters. Starting from the sequence of previously identified Ship2-Sam targeting peptides, an in silico approach was set up to predict higher affinity peptide ligands. A few peptides were experimentally tested through an interdisciplinary approach. Interaction studies were performed by nuclear magnetic resonance spectroscopy and biolayer interferometry. 3D models of Ship2-Sam/peptide complexes were predicted by AlphaFold2. Cell-based assays were carried out to investigate whether such peptide sequences might have an influence on EphA2 signaling. The approach led to the identification of novel Ship2-Sam ligands and shed further light on original approaches to design inhibitors of the Ship2-Sam/EphA2-Sam interaction.

Maximizing the Accuracy of Equilibrium Dissociation Constants for Affinity Complexes: From Theory to Practical Recommendations

The equilibrium dissociation constant (Kd) is a major characteristic of affinity complexes and one of the most frequently determined physicochemical parameters. Despite its significance, the values of Kd obtained for the same complex under similar conditions often exhibit considerable discrepancies and sometimes vary by orders of magnitude. These inconsistencies highlight the susceptibility of Kd determination to large systematic errors, even when random errors are small. It is imperative to both minimize and quantitatively assess the systematic errors inherent in Kd determination. Traditionally, Kd values are determined through nonlinear regression of binding isotherms. This analysis utilizes three variables: concentrations of two reactants and a fraction R of unbound limiting reactant. The systematic errors in Kd arise directly from systematic errors in these variables. Therefore, to maximize the accuracy of Kd, this study thoroughly analyzes the sources of systematic errors within the three variables, including (i) non-additive signals to calculate R, (ii) mis-calibrated experimental instruments, (iii) inaccurate calibration parameters, (iv) insufficient incubation time, (v) unsaturated binding isotherm, (vi) impurities in the reactants, and (vii) solute adsorption onto surfaces. Through this analysis, we illustrate how each source contributes to inaccuracies in the determination of Kd and propose strategies to minimize these contributions. Additionally, we introduce a method for quantitatively assessing the confidence intervals of systematic errors in concentrations, a crucial step toward quantitatively evaluating the accuracy of Kd. While presenting original findings, this paper also reiterates the fundamentals of Kd determination, hence guiding researchers across all proficiency levels. By shedding light on the sources of systematic errors and offering strategies for their mitigation, our work will help researchers enhance the accuracy of Kd determination, thereby making binding studies more reliable and the conclusions drawn from such studies more robust.

An NMR Toolkit to Probe Amyloid Oligomer Inhibition in Neurodegenerative Diseases: From Ligand Screening to Dissecting Binding Topology and Mechanisms of Action

The aggregation of amyloid peptides and proteins into toxic oligomers is a hallmark of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Machado-Joseph's disease, and transmissible spongiform encephalopathies. Inhibition of amyloid oligomers formation and interactions with biological counterparts, as well as the triggering of non-toxic amorphous aggregates, are strategies towards preventive interventions against these pathologies. NMR spectroscopy addresses the need for structural characterization of amyloid proteins and their aggregates, their binding to inhibitors, and rapid screening of compound libraries for ligand identification. Here we briefly discuss the solution experiments constituting the NMR spectroscopist's toolkit and provide examples of their application.

A review of biophysical strategies to investigate protein-ligand binding: What have we employed?

The protein-ligand binding frequently occurs in living organisms and plays a crucial role in the execution of the functions of proteins and drugs. It is also an indispensable part of drug discovery and screening. While the methods for investigating protein-ligand binding are diverse, each has its own objectives, strengths, and limitations, which all influence the choice of method. Many studies concentrate on one or a few specific methods, suggesting that comprehensive summaries are lacking. Therefore in this review, these methods are comprehensively summarized and are discussed in detail: prediction and simulation methods, thermal and thermodynamic methods, spectroscopic methods, methods of determining three-dimensional structures of the complex, mass spectrometry-based methods and others. It is also important to integrate these methods based on the specific objectives of the research. With the aim of advancing pharmaceutical research, this review seeks to deepen the understanding of the protein-ligand binding process.

COSMIC: Molecular Conformation Space Modeling in Internal Coordinates with an Adversarial Framework

The fast and accurate conformation space modeling is an essential part of computational approaches for solving ligand and structure-based drug discovery problems. Recent state-of-the-art diffusion models for molecular conformation generation show promising distribution coverage and physical plausibility metrics but suffer from a slow sampling procedure. We propose a novel adversarial generative framework, COSMIC, that shows comparable generative performance but provides a time-efficient sampling and training procedure. Given a molecular graph and random noise, the generator produces a conformation in two stages. First, it constructs a conformation in a rotation and translation invariant representation─internal coordinates. In the second step, the model predicts the distances between neighboring atoms and performs a few fast optimization steps to refine the initial conformation. The proposed model considers conformation energy, achieving comparable space coverage, and diversity metrics results.

Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements

Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

NMR 1H,19F-based screening of the four stem-looped structure 5_SL1-SL4 located in the 5'-untranslated region of SARS-CoV 2 RNA

Development of new antiviral medication against the beta-coronavirus SARS-CoV-2 (SCoV2) is actively being pursued. Both NMR spectroscopy and crystallography as structural screening technologies have been utilised to screen the viral proteome for binding to fragment libraries. Here, we report on NMR screening of elements of the viral RNA genome with two different ligand libraries using 1H-NMR-screening experiments and 1H and 19F NMR-screening experiments for fluorinated compounds. We screened against the 5'-terminal 119 nucleotides located in the 5'-untranslated region of the RNA genome of SCoV2 and further dissected the four stem-loops into its constituent RNA elements to test specificity of binding of ligands to shorter and longer viral RNA stretches. The first library (DRTL-F library) is enriched in ligands binding to RNA motifs, while the second library (DSI-poised library) represents a fragment library originally designed for protein screening. Conducting screens with two different libraries allows us to compare different NMR screening methodologies, describe NMR screening workflows, validate the two different fragment libraries, and derive initial leads for further downstream medicinal chemistry optimisation.

CarsiDock: a deep learning paradigm for accurate protein-ligand docking and screening based on large-scale pre-training

The expertise accumulated in deep neural network-based structure prediction has been widely transferred to the field of protein-ligand binding pose prediction, thus leading to the emergence of a variety of deep learning-guided docking models for predicting protein-ligand binding poses without relying on heavy sampling. However, their prediction accuracy and applicability are still far from satisfactory, partially due to the lack of protein-ligand binding complex data. To this end, we create a large-scale complex dataset containing ∼9 M protein-ligand docking complexes for pre-training, and propose CarsiDock, the first deep learning-guided docking approach that leverages pre-training of millions of predicted protein-ligand complexes. CarsiDock contains two main stages, i.e., a deep learning model for the prediction of protein-ligand atomic distance matrices, and a translation, rotation and torsion-guided geometry optimization procedure to reconstruct the matrices into a credible binding pose. The pre-training and multiple innovative architectural designs facilitate the dramatically improved docking accuracy of our approach over the baselines in terms of multiple docking scenarios, thereby contributing to its outstanding early recognition performance in several retrospective virtual screening campaigns. Further explorations demonstrate that CarsiDock can not only guarantee the topological reliability of the binding poses but also successfully reproduce the crucial interactions in crystalized structures, highlighting its superior applicability.

Ligand Modification-Free Methods for the Profiling of Protein-Environmental Chemical Interactions

Adverse health outcomes caused by environmental chemicals are often initiated via their interactions with proteins. Essentially, one environmental chemical may interact with a number of proteins and/or a protein may interact with a multitude of environmental chemicals, forming an intricate interaction network. Omics-wide protein-environmental chemical interaction profiling (PECI) is of prominent importance for comprehensive understanding of these interaction networks, including the toxicity mechanisms of action (MoA), and for providing systematic chemical safety assessment. However, such information remains unknown for most environmental chemicals, partly due to their vast chemical diversity. In recent years, with the continuous efforts afforded, especially in mass spectrometry (MS) based omics technologies, several ligand modification-free methods have been developed, and new attention for systematic PECI profiling was gained. In this Review, we provide a comprehensive overview on these methodologies for the identification of ligand-protein interactions, including affinity interaction-based methods of affinity-driven purification, covalent modification profiling, and activity-based protein profiling (ABPP) in a competitive mode, physicochemical property changes assessment methods of ligand-directed nuclear magnetic resonance (ligand-directed NMR), MS integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS), thermal proteome profiling (TPP), limited proteolysis-coupled mass spectrometry (LiP-MS), stability of proteins from rates of oxidation (SPROX), and several intracellular downstream response characterization methods. We expect that the applications of these ligand modification-free technologies will drive a considerable increase in the number of PECI identified, facilitate unveiling the toxicological mechanisms, and ultimately contribute to systematic health risk assessment of environmental chemicals.

Review on NMR as a tool to analyse natural products extract directly: Molecular structure elucidation and biological activity analysis

INTRODUCTION

Natural products, the small organic molecules produced by plants, microbes and invertebrates, often present in the form of a mixture, this leads to the structural characterisation of natural extracts often requiring time-consuming multistep purification procedures. Nuclear magnetic resonance (NMR) technology is traditionally utilised as a tool for the structural elucidation of pure compounds. Contemporarily, an up-to-date trend in the application of NMR in natural product research is shifting to the direct NMR analysis of crude mixtures, to obtain molecular structure and biological activity information without performing cumbersome separation.

OBJECTIVE

To review works of literature on the evolution, principle and progress of NMR technologies for analysing mixtures, we focus on the successful application of NMR technologies in direct analysis of natural product extracts.

METHODOLOGY

Based on our research experience, academic tracking and extensive literature search, which involved, but not limited to, the use of various databases, like Web of Knowledge and PubMed. The keywords used, in various combinations, to retrieve recent literature on the successful application of NMR technologies to sheer natural product extracts, and excluded artificially natural product mixture and biofluid.

RESULTS

NMR technologies for direct natural extracts analysis, including two-dimensional J-resolved spectroscopy (2D-JRES), pure shift NMR, diffusion-ordered NMR spectroscopy (DOSY), statistical correlation spectroscopy (STOCSY), concentration-ordered NMR spectroscopy (CORDY), saturation transfer difference (STD) and water-ligand observed via gradient spectroscopy (WaterLOGSY) were illustrated.

CONCLUSIONS

By these methods, molecular structure and biological activity information will be directly obtained from NMR analysis of natural products extract, aiming to save experimental time and expenses.

Optically Enhanced Solid-State 1H NMR Spectroscopy

Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor-acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hyperpolarization. Systems that exhibit photo-CIDNP in solids are not common, and this effect has, up to now, only been observed for ¹³C and ¹⁵N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here, we report the first example of optically enhanced solid-state ¹H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor-chromophore-acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled ¹H nuclei relays polarization through the whole sample, yielding a 16-fold bulk ¹H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

Degradants of Tenofovir Disoproxil Fumarate Under Forced Yet Mild Thermal Stress: Isolation, Comprehensive Structural Elucidation, and Mechanism

In addition to understanding the mechanism of action for a specific drug candidate, information regarding degradation pathways/products under various stress conditions is essential to know about their short- and long-term effects on health and environment. In line with that, tenofovir disoproxil fumarate (TDF, a co-crystal form of the prodrug tenofovir with fumaric acid), particularly used as an antiretroviral drug for treatment of HIV and hepatitis-B among others, is subjected to primarily thermal and other ICH-prescribed forced degradation conditions and their various degradation products are identified. Upon thermal degradation at 60°C for 8 h, five different degradants (namely DP-1 to DP-5) are isolated, and their structures are unambiguously confirmed using advanced analytical and spectroscopic techniques including ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), high-resolution mass spectrometry (HRMS), state-of-the-art 1- and 2-dimensional nuclear magnetic resonance (1D and 2D NMR), and Fourier-transform infrared spectroscopic (FT-IR) techniques. Among fully characterized five degradants, two new degradants (DP-2 and DP-4) are identified which can potentially impact the stability of TDF via different pathways. Plausible mechanisms leading to all five thermal degradation products are also proposed including the generation of carcinogenic formaldehyde for some cases. The present systematic structural study especially combining MS and advanced NMR investigations unequivocally confirms the structures of the degradants and opens opportunities for connecting the various degradation pathways especially for the TDF-related pharmaceutical candidates.

A Flexible, Modular Platform for Multidimensional Ion Mobility of Native-like Ions

Native ion mobility (IM) mass spectrometry (MS) is used to probe the size, shape, and assembly of biomolecular complexes. IM-IM-MS can increase the amount of information available in structural studies by isolating subpopulations of structures for further analysis. Previously, IM-IM-MS has been implemented using the Structures for Lossless Ion Manipulations (SLIM) architecture to probe the structural stability of gas-phase protein ions. Here, a new multidimensional IM instrument constructed from SLIM devices is characterized using multiple operational modes. In this new design, modular devices are used to perform all ion manipulations, including initial accumulation, injection, separation, selection, and trapping. Using single-dimension IM, collision cross section (Ω) values are determined for a set of native-like ions. These Ω values are within 3% of those reported previously based on measurements using RF-confining drift cells. Tandem IM experiments are performed on a sample of ubiquitin ions that contains both compact and partially unfolded structures, demonstrating that this platform can isolate subpopulations of structures. Finally, additional modes of analysis, including multiplexed IM and inverse IM, are demonstrated using this platform. The ability of this platform to quickly switch between different modes of IM analysis makes it a highly flexible tool for studying protein structures and dynamics.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Parahydrogen-induced polarization allows 2000-fold signal enhancement in biologically active derivatives of the peptide-based drug octreotide

Octreotide, a somatostatin analogue, has shown its efficacy for the diagnostics and treatment of various types of cancer, i.e., in octreotide scan, as radio-marker after labelling with a radiopharmaceutical. To avoid toxicity of radio-labeling, octreotide-based assays can be implemented into magnetic resonance techniques, such as MRI and NMR. Here we used a Parahydrogen-Induced Polarization (PHIP) approach as a cheap, fast and straightforward method. Introduction of L-propargyl tyrosine as a PHIP marker at different positions of octreotide by manual Solid-Phase Peptide Synthesis (SPPS) led to up to 2000-fold proton signal enhancement (SE). Cell binding studies confirmed that all octreotide variants retained strong binding affinity to the surface of human-derived cancer cells expressing somatostatin receptor 2. The hydrogenation reactions were successfully performed in methanol and under physiologically compatible mixtures of water with methanol or ethanol. The presented results open up new application areas of biochemical and pharmacological studies with octreotide.

First in vivo fluorine-19 magnetic resonance imaging of the multiple sclerosis drug siponimod

Theranostic imaging methods could greatly enhance our understanding of the distribution of CNS-acting drugs in individual patients. Fluorine-19 magnetic resonance imaging (¹⁹F MRI) offers the opportunity to localize and quantify fluorinated drugs non-invasively, without modifications and without the application of ionizing or other harmful radiation. Here we investigated siponimod, a sphingosine 1-phosphate (S₁P) receptor antagonist indicated for secondary progressive multiple sclerosis (SPMS), to determine the feasibility of in vivo ¹⁹F MR imaging of a disease modifying drug. Methods: The ¹⁹F MR properties of siponimod were characterized using spectroscopic techniques. Four MRI methods were investigated to determine which was the most sensitive for ¹⁹F MR imaging of siponimod under biological conditions. We subsequently administered siponimod orally to 6 mice and acquired ¹⁹F MR spectra and images in vivo directly after administration, and in ex vivo tissues. Results: The ¹⁹F transverse relaxation time of siponimod was 381 ms when dissolved in dimethyl sulfoxide, and substantially reduced to 5 ms when combined with serum, and to 20 ms in ex vivo liver tissue. Ultrashort echo time (UTE) imaging was determined to be the most sensitive MRI technique for imaging siponimod in a biological context and was used to map the drug in vivo in the stomach and liver. Ex vivo images in the liver and brain showed an inhomogeneous distribution of siponimod in both organs. In the brain, siponimod accumulated predominantly in the cerebrum but not the cerebellum. No secondary ¹⁹F signals were detected from metabolites. From a translational perspective, we found that acquisitions done on a 3.0 T clinical MR scanner were 2.75 times more sensitive than acquisitions performed on a preclinical 9.4 T MR setup when taking changes in brain size across species into consideration and using equivalent relative spatial resolution. Conclusion: Siponimod can be imaged non-invasively using ¹⁹F UTE MRI in the form administered to MS patients, without modification. This study lays the groundwork for more extensive preclinical and clinical investigations. With the necessary technical development, ¹⁹F MRI has the potential to become a powerful theranostic tool for studying the time-course and distribution of CNS-acting drugs within the brain, especially during pathology.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

New Development in Understanding Drug-Polymer Interactions in Pharmaceutical Amorphous Solid Dispersions from Solid-State Nuclear Magnetic Resonance

Pharmaceutical amorphous solid dispersions (ASDs) represent a widely used technology to increase the bioavailability of active pharmaceutical ingredients (APIs). ASDs are based on an amorphous API dispersed in a polymer, and their stability is driven by the presence of strong intermolecular interactions between these two species (e.g., hydrogen bond, electrostatic interactions, etc.). The understanding of these interactions at the atomic level is therefore crucial, and solid-state nuclear magnetic resonance (NMR) has demonstrated itself as a very powerful technique for probing API-polymer interactions. Other reviews have also reported exciting approaches to study the structures and dynamic properties of ASDs and largely focused on the study of API-polymer miscibility and on the identification of API-polymer interactions. Considering the increased use of NMR in the field, the aim of this Review is to specifically highlight recent experimental strategies used to identify API-polymer interactions and report promising recent examples using one-dimensional (1D) and two-dimensional (2D) experiments by exploiting the following emerging approaches of very-high magnetic field and ultrafast magic angle spinning (MAS). A range of different ASDs spanning APIs and polymers with varied structural motifs is targeted to illustrate new ways to understand the mechanism of stability of ASDs to enable the design of new dispersions.

Binding-driven reactivity attenuation enables NMR identification of selective drug candidates for nucleic acid targets

NMR methods, and in particular ligand-based approaches, are among the most robust and reliable alternatives for binding detection and consequently, they have become highly popular in the context of hit identification and drug discovery. However, when dealing with DNA/RNA targets, these techniques face limitations that have precluded widespread application in medicinal chemistry. In order to expand the arsenal of spectroscopic tools for binding detection and to overcome the existing difficulties, herein we explore the scope and limitations of a strategy that makes use of a binding indicator previously unexploited by NMR: the perturbation of the ligand reactivity caused by complex formation. The obtained results indicate that ligand reactivity can be utilised to reveal association processes and identify the best binders within mixtures of significant complexity, providing a conceptually different reactivity-based alternative within NMR screening methods.

Solvent paramagnetic relaxation enhancement as a versatile method for studying structure and dynamics of biomolecular systems

Solvent paramagnetic relaxation enhancement (sPRE) is a versatile nuclear magnetic resonance (NMR)-based method that allows characterization of the structure and dynamics of biomolecular systems through providing quantitative experimental information on solvent accessibility of NMR-active nuclei. Addition of soluble paramagnetic probes to the solution of a biomolecule leads to paramagnetic relaxation enhancement in a concentration-dependent manner. Here we review recent progress in the sPRE-based characterization of structural and dynamic properties of biomolecules and their complexes, and aim to deliver a comprehensive illustration of a growing number of applications of the method to various biological systems. We discuss the physical principles of sPRE measurements and provide an overview of available co-solute paramagnetic probes. We then explore how sPRE, in combination with complementary biophysical techniques, can further advance biomolecular structure determination, identification of interaction surfaces within protein complexes, and probing of conformational changes and low-population transient states, as well as deliver insights into weak, nonspecific, and transient interactions between proteins and co-solutes. In addition, we present examples of how the incorporation of solvent paramagnetic probes can improve the sensitivity of NMR experiments and discuss the prospects of applying sPRE to NMR metabolomics, drug discovery, and the study of intrinsically disordered proteins.

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.

Tools shaping drug discovery and development

Spectroscopic, scattering, and imaging methods play an important role in advancing the study of pharmaceutical and biopharmaceutical therapies. The tools more familiar to scientists within industry and beyond, such as nuclear magnetic resonance and fluorescence spectroscopy, serve two functions: as simple high-throughput techniques for identification and purity analysis, and as potential tools for measuring dynamics and structures of complex biological systems, from proteins and nucleic acids to membranes and nanoparticle delivery systems. With the expansion of commercial small-angle x-ray scattering instruments into the laboratory setting and the accessibility of industrial researchers to small-angle neutron scattering facilities, scattering methods are now used more frequently in the industrial research setting, and probe-less time-resolved small-angle scattering experiments are now able to be conducted to truly probe the mechanism of reactions and the location of individual components in complex model or biological systems. The availability of atomic force microscopes in the past several decades enables measurements that are, in some ways, complementary to the spectroscopic techniques, and wholly orthogonal in others, such as those related to nanomechanics. As therapies have advanced from small molecules to protein biologics and now messenger RNA vaccines, the depth of biophysical knowledge must continue to serve in drug discovery and development to ensure quality of the drug, and the characterization toolbox must be opened up to adapt traditional spectroscopic methods and adopt new techniques for unraveling the complexities of the new modalities. The overview of the biophysical methods in this review is meant to showcase the uses of multiple techniques for different modalities and present recent applications for tackling particularly challenging situations in drug development that can be solved with the aid of fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, and small-angle scattering.

Boosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer

The past few years have witnessed enormous progress toward applying machine learning approaches to the development of protein-ligand scoring functions. However, the robust performance and wide applicability of scoring functions remain a big challenge for increasing the success rate of docking-based virtual screening. Herein, a novel scoring function named RTMScore was developed by introducing a tailored residue-based graph representation strategy and several graph transformer layers for the learning of protein and ligand representations, followed by a mixture density network to obtain residue-atom distance likelihood potential. Our approach was resolutely validated on the CASF-2016 benchmark, and the results indicate that RTMScore can outperform almost all of the other state-of-the-art methods in terms of both the docking and screening powers. Further evaluation confirms the robustness of our approach that can not only retain its docking power on cross-docked poses but also achieve improved performance as a rescoring tool in larger-scale virtual screening.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Radio Signals from Live Cells: The Coming of Age of In-Cell Solution NMR

A detailed knowledge of the complex processes that make cells and organisms alive is fundamental in order to understand diseases and to develop novel drugs and therapeutic treatments. To this aim, biological macromolecules should ideally be characterized at atomic resolution directly within the cellular environment. Among the existing structural techniques, solution NMR stands out as the only one able to investigate at high resolution the structure and dynamic behavior of macromolecules directly in living cells. With the advent of more sensitive NMR hardware and new biotechnological tools, modern in-cell NMR approaches have been established since the early 2000s. At the coming of age of in-cell NMR, we provide a detailed overview of its developments and applications in the 20 years that followed its inception. We review the existing approaches for cell sample preparation and isotopic labeling, the application of in-cell NMR to important biological questions, and the development of NMR bioreactor devices, which greatly increase the lifetime of the cells allowing real-time monitoring of intracellular metabolites and proteins. Finally, we share our thoughts on the future perspectives of the in-cell NMR methodology.

A Selective Alkylating Agent for CTG Repeats in Myotonic Dystrophy Type 1

Disease intervention at the DNA level generally has been avoided because of off-target effects. Recent advances in genome editing technologies using CRISPR-Cas9 have opened a new era in DNA-targeted therapeutic approaches. However, delivery of such systems remains a major challenge. Here, we report a selective DNA-modifying small molecule that targets a disease-specific structure and mismatches involved in myotonic dystrophy type 1 (DM1). This ligand alkylates T-T mismatch-containing hairpins formed in the expanded CTG repeats (d(CTG)^exp) in DM1. Ligand alkylation of d(CTG)^exp inhibits the transcription of d(CAG·CTG)^exp, thereby reducing the level of the toxic r(CUG)^exp transcript. The bioactivity of the ligand also included a reduction in DM1 pathological features such as disease foci formation and misregulation of pre-mRNA splicing in DM1 model cells. Furthermore, the CTG-alkylating ligand may change the d(CAG·CTG)^exp repeat length dynamics in DM1 patient cells. Our strategy of linking an alkylating moiety to a DNA mismatch-selective small molecule may be generally applicable to other repeat expansion diseases such as Huntington's disease and amyotrophic lateral sclerosis.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.

Probing the Functional Interaction Interface of Lipopolysaccharide and Antimicrobial Peptides: A Solution-State NMR Perspective

Antimicrobial peptides (AMPs) have been a topic of substantial research as the next-generation antibiotics. They have been extensively studied for the selectivity and action against microbial membrane lipids in imparting their targeted functioning. To determine the effectivity of the peptides against the Gram-negative pathogens, it is imperative to elucidate their role in interacting with the lipopolysaccharide moieties. Lipopolysaccharide is a major component of the outer membrane of the Gram-negative bacteria. It serves to protect the bacteria as well as govern the functionality of several antibacterial agents. It can prevent the access of the agents into the inner membrane of the bacteria, thus rendering them inactive. Several techniques have been employed to study the interaction for better designing of peptides; NMR spectroscopy is one of the most widely used techniques in determining the interactive properties of peptides with LPS as it can provide the details in atomistic level. NMR spectroscopy provides information about the structural and conformational changes as well as the dynamics of the interactions.

Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server

Nuclear magnetic resonance (NMR) is an effective, commonly used experimental approach to screen small organic molecules against a protein target. A very popular method consists of monitoring the changes of the NMR chemical shifts of the protein nuclei upon addition of the small molecule to the free protein. Multidimensional NMR experiments allow the interacting residues to be mapped along the protein sequence. A significant amount of human effort goes into manually tracking the chemical shift variations, especially when many signals exhibit chemical shift changes and when many ligands are tested. Some computational approaches to automate the procedure are available, but none of them as a web server. Furthermore, some methods require the adoption of a fairly specific experimental setup, such as recording a series of spectra at increasing small molecule:protein ratios. In this work, we developed a tool requesting a minimal amount of experimental data from the user, implemented it as an open-source program, and made it available as a web application. Our tool compares two spectra, one of the free protein and one of the small molecule:protein mixture, based on the corresponding peak lists. The performance of the tool in terms of correct identification of the protein-binding regions has been evaluated on different protein targets, using experimental data from interaction studies already available in the literature. For a total of 16 systems, our tool achieved between 79% and 100% correct assignments, properly identifying the protein regions involved in the interaction.

Structure-based protein-ligand interaction fingerprints for binding affinity prediction

Binding affinity prediction (BAP) using protein–ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein–ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.

Label-free flow cytometry-based enzyme inhibitor identification

Fluorescence-based methods for the identification of enzyme inhibitors are widespread, but usually require protein or ligand labelling. In this study, we present a label-free displacement assay that takes advantage of the intrinsic fluorescence of a tight binding ligand avoiding any labeling. Autodisplay-based accessibility of the target enzyme on the cell surface of Escherichia coli enabled the quantification of fluorescent ligand binding by flow cytometry. Human protein kinase CK2 was used as proof-of-concept enzyme and its ATP competitive inhibitor (E)-1,3-dichloro-6-[(4-methoxyphenylimino)methyl]dibenzo[b,d]furan-2,7-diol (compound 5) was shown to exhibit intrinsic fluorescence (λ_max(ex) = 370 nm, λ_max(em) = 585 nm). Binding of compound 5 to CK2 displaying cells was quantified via flow cytometry with linearly increasing relative fluorescence up to a concentration of 1.25 μM. The addition of the non-fluorescent CK2 inhibitor 4,5,6,7-tetrabromobenzotriazole (TBB) competed for compound 5 binding with a half maximal fluorescence reduction at 15.6 μM TBB. This new and simple binding assay provides a valuable tool for the screening of high affinity enzyme inhibitors, overcoming the limitations of fluorescent ligand labelling.

Drug-Polymer Interactions in Acetaminophen/Hydroxypropylmethylcellulose Acetyl Succinate Amorphous Solid Dispersions Revealed by Multidimensional Multinuclear Solid-State NMR Spectroscopy

The bioavailability of insoluble crystalline active pharmaceutical ingredients (APIs) can be enhanced by formulation as amorphous solid dispersions (ASDs). One of the key factors of ASD stabilization is the formation of drug-polymer interactions at the molecular level. Here, we used a range of multidimensional and multinuclear nuclear magnetic resonance (NMR) experiments to identify these interactions in amorphous acetaminophen (paracetamol)/hydroxypropylmethylcellulose acetyl succinate (HPMC-AS) ASDs at various drug loadings. At low drug loading (<20 wt %), we showed that 1H-13C through-space heteronuclear correlation experiments identify proximity between aromatic protons in acetaminophen with cellulose backbone protons in HPMC-AS. We also show that 14N-1H heteronuclear multiple quantum coherence (HMQC) experiments are a powerful approach in probing spatial interactions in amorphous materials and establish the presence of hydrogen bonds (H-bond) between the amide nitrogen of acetaminophen with the cellulose ring methyl protons in these ASDs. In contrast, at higher drug loading (40 wt %), no acetaminophen/HPMC-AS spatial proximity was identified and domains of recrystallization of amorphous acetaminophen into its crystalline form I, the most thermodynamically stable polymorph, and form II are identified. These results provide atomic scale understanding of the interactions in the acetaminophen/HPMC-AS ASD occurring via H-bond interactions.

Exploring the Druggability of Conserved RNA Regulatory Elements in the SARS-CoV-2 Genome

SARS-CoV-2 contains a positive single-stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS-CoV and SARS-CoV-2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex-vivo structural probing experiments. These elements contain non-base-paired regions that potentially harbor ligand-binding pockets. Here, we performed an NMR-based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1 H-based 1D NMR binding assays. The screening identified common as well as RNA-element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS-CoV-2.

NITD-688, a pan-serotype inhibitor of the dengue virus NS4B protein, shows favorable pharmacokinetics and efficacy in preclinical animal models

Dengue virus (DENV) is a mosquito-borne flavivirus that poses a threat to public health, yet no antiviral drug is available. We performed a high-throughput phenotypic screen using the Novartis compound library and identified candidate chemical inhibitors of DENV. This chemical series was optimized to improve properties such as anti-DENV potency and solubility. The lead compound, NITD-688, showed strong potency against all four serotypes of DENV and demonstrated excellent oral efficacy in infected AG129 mice. There was a 1.44-log reduction in viremia when mice were treated orally at 30 milligrams per kilogram twice daily for 3 days starting at the time of infection. NITD-688 treatment also resulted in a 1.16-log reduction in viremia when mice were treated 48 hours after infection. Selection of resistance mutations and binding studies with recombinant proteins indicated that the nonstructural protein 4B is the target of NITD-688. Pharmacokinetic studies in rats and dogs showed a long elimination half-life and good oral bioavailability. Extensive in vitro safety profiling along with exploratory rat and dog toxicology studies showed that NITD-688 was well tolerated after 7-day repeat dosing, demonstrating that NITD-688 may be a promising preclinical candidate for the treatment of dengue.

Development of a Potential Yeast-Based Vaccine Platform for Theileria parva Infection in Cattle

East Coast Fever (ECF), caused by the tick-borne apicomplexan parasite Theileria parva, remains one of the most important livestock diseases in sub-Saharan Africa with more than 1 million cattle dying from infection every year. Disease prevention relies on the so-called "Infection and Treatment Method" (ITM), which is costly, complex, laborious, difficult to standardise on a commercial scale and results in a parasite strain-specific, MHC class I-restricted cytotoxic T cell response. We therefore attempted to develop a safe, affordable, stable, orally applicable and potent subunit vaccine for ECF using five different T. parva schizont antigens (Tp1, Tp2, Tp9, Tp10 and N36) and Saccharomyces cerevisiae as an expression platform. Full-length Tp2 and Tp9 as well as fragments of Tp1 were successfully expressed on the surface of S. cerevisiae. In vitro analyses highlighted that recombinant yeast expressing Tp2 can elicit IFNγ responses using PBMCs from ITM-immunized calves, while Tp2 and Tp9 induced IFNγ responses from enriched bovine CD8⁺ T cells. A subsequent in vivo study showed that oral administration of heat-inactivated, freeze-dried yeast stably expressing Tp2 increased total murine serum IgG over time, but more importantly, induced Tp2-specific serum IgG antibodies in individual mice compared to the control group. While these results will require subsequent experiments to verify induction of protection in neonatal calves, our data indicates that oral application of yeast expressing Theileria antigens could provide an affordable and easy vaccination platform for sub-Saharan Africa. Evaluation of antigen-specific cellular immune responses, especially cytotoxic CD8⁺ T cell immunity in cattle will further contribute to the development of a yeast-based vaccine for ECF.

NMR-based screening for natural product subfraction to precisely identify ligand of target protein

INTRODUCTION

The inherent diversity of natural product extracts has not only made their value for biological activity attractive, but has also presented significant challenges for separation and detection techniques to enable rapid characterisation of the biologically active component present in the complicated mixture.

OBJECTIVE

Nuclear magnetic resonance (NMR) is routinely valued for its ability to shed light on molecular structure, when NMR is used as a promising tool in drug screening, it can detect and quantify molecular interactions, and at the same time provide detailed structural information with atomic level resolution. Here, we introduced an accurate and efficient strategy for discovering ligands from natural product extracts, by taking the advantage of NMR-based drug screening.

METHODOLOGY

The characteristic pre-purified subfraction libraries were brought into screening, and combinatorial ligand-observed NMR interaction detection experiments were performed, once hits were found from one subfraction, the repository of the subfraction would be subject to separation and preparation, and the structure of the hits would be easily identified.

RESULTS

Screening procedure of Radix Polygoni Multiflori water extract against human serum albumin (HSA) was used as an example, to discuss and verify the detailed methodology. Furthermore, human fatty acid binding protein 4 (FABP₄ ) was presented as an example target protein, to illustrate the utility of this method for discovering biologically active component.

CONCLUSIONS

It is proved that suitable subfraction library and well-combined ligand-detected NMR experiments will make the screening streamline more accurate and efficient. NMR is a promising tool to integrate natural product extracts into modern drug screening.

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.

Why and How to Dig into Plant Metabolite-Protein Interactions

Interaction between metabolites and proteins drives cellular regulatory processes within and between organisms. Recent reports highlight that numerous plant metabolites embrace multiple biological activities, beyond a sole role as substrates, products, or cofactors of enzymes, or as defense or growth-regulatory compounds. Though several technologies have been developed to identify and characterize metabolite-protein interactions, the systematic implementation of such methods in the plant field remains limited. Here, we discuss the plant metabolic space, with a specific focus on specialized metabolites and their roles, and review the technologies to study their interaction with proteins. We approach it both from a plant's perspective, to increase our understanding of plant metabolite-dependent regulatory networks, and from a human perspective, to empower agrochemical and drug discoveries.

Non-Carbohydrate Glycomimetics as Inhibitors of Calcium(II)-Binding Lectins

Because of the antimicrobial resistance crisis, lectins are considered novel drug targets. Pseudomonas aeruginosa utilizes LecA and LecB in the infection process. Inhibition of both lectins with carbohydrate-derived molecules can reduce biofilm formation to restore antimicrobial susceptibility. Here, we focused on non-carbohydrate inhibitors for LecA to explore new avenues for lectin inhibition. From a screening cascade we obtained one experimentally confirmed hit, a catechol, belonging to the well-known PAINS compounds. Rigorous analyses validated electron-deficient catechols as millimolar LecA inhibitors. The first co-crystal structure of a non-carbohydrate inhibitor in complex with a bacterial lectin clearly demonstrates the catechol mimicking the binding of natural glycosides with LecA. Importantly, catechol 3 is the first non-carbohydrate lectin ligand that binds bacterial and mammalian calcium(II)-binding lectins, giving rise to this fundamentally new class of glycomimetics.

Dendrimers as Color-Stabilizers of Pyranoanthocyanins: The Dye Concentration Governs the Host-Guest Interaction Mechanisms

Anionic dendrimers have recently emerged as hosts (H) for the color stabilization of the flavylium cation of anthocyanin guests (G). The interaction with a promising, more hydrophobic pyranoanthocyanin illustrates how the structure and concentration of the dye modulate the host-guest interaction mechanisms. NMR and UV-vis titrations (host over guest, from G/H ratio 2089 to 45) showed that at relatively low dendrimer-to-dye concentrations, ion pairs at the dendrimer periphery prevail over dye encapsulation. This promotes the deaggregation of the dye, not previously observed with anthocyanins, and related to the more hydrophobic nature of this dye (deshielding of the dye ¹H signals, higher T ₂ relaxation times, constant diffusion coefficient). As the dendrimer concentration increases, the dye encapsulation, earlier unseen with structurally simpler flavylium dyes, becomes dominant (shielding and broadening of the dye ¹H signals and lower T ₂ and diffusion coefficient). The interaction parameters of the encapsulation process (K ∼ 4.51 × 10⁴ M^-1, n ∼ 150) indicate the binding of ca. one pyranoanthocyanin molecule by each sulfate terminal group. Our results provide insights into the ability of dendrimers to host structurally diverse pyranoflavylium-based dyes and how the structure of the latter modulates the range of interactions involved. The encapsulation ability of this dendrimer to such pH-sensitive dyes is envisioned for the host-guest sensing applications such as pH-responsive systems used for example in food smart packaging.

Using NMR to identify binding regions for N and C-terminal Hsp90 inhibitors using Hsp90 domains

We present the first NMR study of the interaction between heat shock protein 90 (Hsp90) and amino (N)-terminal inhibitors 17-AAG, and AUY922, and carboxy (C)-terminal modulators SM253, and LB51. We show that the two ATP mimics, 17-AAG and AUY922, bind deeply within the ATP binding pocket of the N-terminal domain, consistent with the crystal structures. In contrast, SM253, a C-terminal Hsp90 modulator, binds to the linker region between the N and middle domains. We also show that C-terminal inhibitor LB51 binds to the C-terminus with a more significant spectroscopic change than previously reported using NMR binding studies of C-terminal inhibitors novobiocin and silybin. These data provide key insights into how the allosteric inhibitor SM253 controls the C-terminal co-chaperones and confirms the binding domain of LB51.

Applications of Solution NMR in Drug Discovery

During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.

19 F NMR-Based Fragment Screening for 14 Different Biologically Active RNAs and 10 DNA and Protein Counter-Screens

We report here the nuclear magnetic resonance 19 F screening of 14 RNA targets with different secondary and tertiary structure to systematically assess the druggability of RNAs. Our RNA targets include representative bacterial riboswitches that naturally bind with nanomolar affinity and high specificity to cellular metabolites of low molecular weight. Based on counter-screens against five DNAs and five proteins, we can show that RNA can be specifically targeted. To demonstrate the quality of the initial fragment library that has been designed for easy follow-up chemistry, we further show how to increase binding affinity from an initial fragment hit by chemistry that links the identified fragment to the intercalator acridine. Thus, we achieve low-micromolar binding affinity without losing binding specificity between two different terminator structures.