Applications of Solution NMR in Drug Discovery

Advancements in clinical RNA therapeutics: Present developments and prospective outlooks

RNA molecules have emerged as promising clinical therapeutics due to their ability to target "undruggable" proteins or molecules with high precision and minimal side effects. Nevertheless, the primary challenge in RNA therapeutics lies in rapid degradation and clearance from systemic circulation, the inability to traverse cell membranes, and the efficient intracellular delivery of bioactive RNA molecules. In this review, we explore the implications of RNAs in diseases and provide a chronological overview of the development of RNA therapeutics. Additionally, we summarize the technological advances in RNA-screening design, encompassing various RNA databases and design platforms. The paper then presents an update on FDA-approved RNA therapeutics and those currently undergoing clinical trials for various diseases, with a specific emphasis on RNA medicine and RNA vaccines.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

HTS driven by fluorescence lifetime detection of FRET identifies activators and inhibitors of cardiac myosin

Small molecules that bind to allosteric sites on target proteins to alter protein function are highly sought in drug discovery. High-throughput screening (HTS) assays are needed to facilitate the direct discovery of allosterically active compounds. We have developed technology for high-throughput time-resolved fluorescence lifetime detection of fluorescence resonance energy transfer (FRET), which enables the detection of allosteric modulators by monitoring changes in protein structure. We tested this approach at the industrial scale by adapting an allosteric FRET sensor of cardiac myosin to high-throughput screening (HTS), based on technology provided by Photonic Pharma and the University of Minnesota, and then used the sensor to screen 1.6 million compounds in the HTS facility at Bristol Myers Squibb. The results identified allosteric activators and inhibitors of cardiac myosin that do not compete with ATP binding, demonstrating high potential for FLT-based drug discovery.

Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR

Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.

Biophysical Approaches for the Characterization of Protein-Metabolite Interactions

With an estimate of hundred thousands of protein molecules per cell and the number of metabolites several orders of magnitude higher, protein-metabolite interactions are omnipresent. In vitro analyses are one of the main pillars on the way to establish a solid understanding of how these interactions contribute to maintaining cellular homeostasis. A repertoire of biophysical techniques is available by which protein-metabolite interactions can be quantitatively characterized in terms of affinity, specificity, and kinetics in a broad variety of solution environments. Several of those provide information on local or global conformational changes of the protein partner in response to ligand binding. This review chapter gives an overview of the state-of-the-art biophysical toolbox for the study of protein-metabolite interactions. It briefly introduces basic principles, highlights recent examples from the literature, and pinpoints promising future directions.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

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.

Assays Used for Discovering Small Molecule Inhibitors of YAP Activity in Cancers

Simple Summary

Cancer is a disease in which cells grow in an uncontrolled manner. This can be due to excessive cell proliferation or reduced cell death or a combination of the two. The Hippo signaling pathway, when misregulated, promotes excessive growth and cancer development by inducing uncontrolled cell proliferation and inhibiting cell death. This is achieved due to unregulated activity of the oncogenic effector of this pathway, YAP/TAZ. Therefore, it is critical to develop inhibitors to disrupt YAP activity in cancers. This article reviews the different types of assays that are used in development of small molecule inhibitors for YAP activity in cancers.

Abstract

YAP/TAZ are transcriptional coactivators that function as the key downstream effectors of Hippo signaling. They are commonly misregulated in most human cancers, which exhibit a higher level of expression and nuclear localization of YAP/TAZ, and display addiction to YAP-dependent transcription. In the nucleus, these coactivators associate with TEA domain transcription factors (TEAD1-4) to regulate the expression of genes that promote cell proliferation and inhibit cell death. Together, this results in an excessive growth of the cancerous tissue. Further, YAP/TAZ play a critical role in tumor metastasis and chemotherapy resistance by promoting cancer stem cell fate. Furthermore, they affect tumor immunity by promoting the expression of PD-L1. Thus, YAP plays an important role in multiple aspects of cancer biology and thus, provides a critical target for cancer therapy. Here we discuss various assays that are used for conducting high-throughput screens of small molecule libraries for hit identification, and subsequent hit validation for successful discovery of potent inhibitors of YAP-transcriptional activity. Furthermore, we describe the advantages and limitations of these assays.

Screening strategies for identifying RNA- and ribonucleoprotein-targeted compounds

The past few years have witnessed important breakthroughs in the identification of compounds that specifically bind and regulate RNAs and in optimizing them for therapeutic use. Here, we review successful and unsuccessful approaches in screening for RNA-targeted small molecules. We discuss advantages and disadvantages of the different screening techniques and variables that affect the outcome of RNA-screening projects. We also highlight key challenges that hamper the development of quality RNA ligands, especially the still-low availability of RNA-specific compound libraries and the poor understanding of RNA structural dynamics. We conclude that the development of new RNA-targeting drugs would greatly benefit from integration of the power of high-throughput screening technologies with improved biochemical, structural, and computational characterization of RNA targets.

Rh-Catalyzed cascade C-H activation/C-C cleavage/cyclization of carboxylic acids with cyclopropanols

Merging both C-H and C-C activation in a tandem process is a marked challenge. A novel Rh(iii)-catalyzed C-H activation/ring opening C-C cleavage/cyclization of carboxylic acids with cyclopropanols was developed for the synthesis of 3-substituted phthalides and α,β-butenolides. This reaction displays excellent functional group tolerance with respect to both carboxylic acids and cyclopropanols and features relatively mild conditions. Remarkably, the utility of this method was highlighted by the rapid construction of bioactive compounds bearing a 3-substituted phthalide framework via late-stage functionalization.