High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

NMR structures of small molecules bound to a model of an RNA CUG repeat expansion

Trinucleotide repeat expansions fold into long, stable hairpins and cause a variety of incurable RNA gain-of-function diseases such as Huntington's disease, the myotonic dystrophies, and spinocerebellar ataxias. One approach for treating these diseases is to bind small molecules to the structured RNAs. Both Huntington's disease-like 2 (HDL2) and myotonic dystrophy type 1 (DM1) are caused by a r(CUG) repeat expansion, or r(CUG) exp . The RNA folds into a hairpin structure with a periodic array of 1×1 nucleotide UU loops (5'C U G/3'G U C; where the underlined nucleotides indicate the Us in the internal loop) that sequester various RNA-binding proteins (RBP) and hence the source of its gain-of-function. Here, we report NMR-refined structures of single 5'C U G/3'G U C motifs in complex with three different small molecules, a diguandinobenzoate ( 1 ), a derivative of 1 where the guanidino groups have been exchanged for imidazole ( 2 ), and a quinoline with improved drug-like properties ( 3 ). These structures were determined using nuclear magnetic resonance (NMR) spectroscopy and simulated annealing with restrained molecular dynamics (MD). Compounds 1 , 2 , and 3 formed stacking and hydrogen bonding interactions with the 5'C U G/3'G U C motif. Compound 3 also formed van der Waals interactions with the internal loop. The global structure of each RNA-small molecule complexes retains an A-form conformation, while the internal loops are still dynamic but to a lesser extent compared to the unbound form. These results aid our understanding of ligand-RNA interactions and enable structure-based design of small molecules with improved binding affinity for and biological activity against r(CUG) exp . As the first ever reported structures of RNA r(CUG) repeats bound to ligands, these structures can enable virtual screening campaigns combined with machine learning assisted de novo design.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

Forced intercalation-induced light-up peptides as fluorogenic indicators for the HIV-1 TAR RNA-ligand assay

Fluorescence indicators capable of binding to human immunodeficiency virus-1 (HIV-1) trans-activation responsive (TAR) RNA are powerful tools for the exploratory studies of the identification of anti-HIV drug candidates. This work presents a new design strategy for fluorogenic indicators with a transactivator of transcription (Tat)-derived peptide based on the forced intercalation of thiazole orange (TO) dyes (FIT). The developed 9-mer FIT peptide (RKKRR-TO-RRR: named FiLuP) features the TO unit integrated onto a Dap (2,3-diaminopropionic acid) residue in the middle of the Tat peptide sequence; the Q (glutamic acid) residue in the Tat peptide (RKKRR-Q-RRR) is replaced with TO as if it were an amino acid surrogate. This facilitates a significant light-up response (450-fold at λem = 541 nm, Φfree = 0.0057, and Φbound = 0.61) upon binding to TAR RNA. The response of FiLuP is highly selective to TAR RNA over other non-cognate RNAs, and FiLuP maintains strong binding affinity (Kd = 1.0 ± 0.6 nM). Significantly, in contrast to previously developed Tat peptide-based FRET probes, FiLuP is able to discriminate between "competitive" and "noncompetitive" inhibitors when used in the fluorescence indicator displacement (FID) assay. The FID assay under stringent screening conditions is also possible, enabling super-strong competitive binders toward TAR RNA to be sieved out.

Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.

Assessment of Nucleobase Protomeric and Tautomeric States in Nucleic Acid Structures for Interaction Analysis and Structure-Based Ligand Design

With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for pKa and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural C+G:C(A) and A+GG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.

In vitro and silico studies of geraniin interfering with HSV-2 replication by targeting glycoprotein D

Residues ASN94 and GLN41 presented the highest frequency in molecular docking tests. The geraniin-glycoprotein D(gD) complexes was stable with RMSD(root mean square deviation)value less than 0.3 nm. The Molecular dynamic (MD) simulations revealed stable hydrogen bonds between gD and geraniin. Root mean square fluctuation (RMSF) values were less than 0.15 nm around the interface of geraniin-gD complex. In virucidal assays showed a much higher anti-HSV-2 inhibition activity of geraniin as compared to acyclovir(ACV).Human immunodeficiency virus transactivator (HIV-TAT) treatment significantly enhanced HSV-2 replication and lethal effect on HaCaT cells. The inhibitory rate of geraniin against HSV-2 coinfected with HIV-TAT was significantly decreased. The immunofluorescence results also revealed that HSV-2 gD expression presented a green fluorescence on HaCaT cells membranes and showed clear downregulation in geraniin-treated cells, but was expressed clearly on cell membranes under geraniin, HSV-2 and HIV-TAT cotreatment. The anti-apoptotic effect from geraniin persisted after 72 h, while the anti-apoptotic effect from geraniin diminished when HIV-TAT and geraniin were combined.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

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.

Heterobifunctional small molecules to modulate RNA function

RNA has diverse cellular functionality, including regulating gene expression, protein translation, and cellular response to stimuli, due to its intricate structures. Over the past decade, small molecules have been discovered that target functional structures within cellular RNAs and modulate their function. Simple binding, however, is often insufficient, resulting in low or even no biological activity. To overcome this challenge, heterobifunctional compounds have been developed that can covalently bind to the RNA target, alter RNA sequence, or induce its cleavage. Herein, we review the recent progress in the field of RNA-targeted heterobifunctional compounds using representative case studies. We identify critical gaps and limitations and propose a strategic pathway for future developments of RNA-targeted molecules with augmented functionalities.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Small molecule approaches to targeting RNA

The development of innovative methodologies to identify RNA binders has attracted enormous attention in chemical biology and drug discovery. Although antibiotics targeting bacterial ribosomal RNA have been on the market for decades, the renewed interest in RNA targeting reflects the need to better understand complex intracellular processes involving RNA. In this context, small molecules are privileged tools used to explore the biological functions of RNA and to validate RNAs as therapeutic targets, and they eventually are to become new drugs. Despite recent progress, the rational design of specific RNA binders requires a better understanding of the interactions which occur with the RNA target to reach the desired biological response. In this Review, we discuss the challenges to approaching this underexplored chemical space, together with recent strategies to bind, interact and affect biologically relevant RNAs.

Structural and computational studies of HIV-1 RNA

Viruses remain a global threat to animals, plants, and humans. The type 1 human immunodeficiency virus (HIV-1) is a member of the retrovirus family and carries an RNA genome, which is reverse transcribed into viral DNA and further integrated into the host-cell DNA for viral replication and proliferation. The RNA structures from the HIV-1 genome provide valuable insights into the mechanisms underlying the viral replication cycle. Moreover, these structures serve as models for designing novel therapeutic approaches. Here, we review structural data on RNA from the HIV-1 genome as well as computational studies based on these structural data. The review is organized according to the type of structured RNA element which contributes to different steps in the viral replication cycle. This is followed by an overview of the HIV-1 transactivation response element (TAR) RNA as a model system for understanding dynamics and interactions in the viral RNA systems. The review concludes with a description of computational studies, highlighting the impact of biomolecular simulations in elucidating the mechanistic details of various steps in the HIV-1's replication cycle.

An RNA excited conformational state at atomic resolution

Sparse and short-lived excited RNA conformational states are essential players in cell physiology, disease, and therapeutic development, yet determining their 3D structures remains challenging. Combining mutagenesis, NMR spectroscopy, and computational modeling, we determined the 3D structural ensemble formed by a short-lived (lifetime ~2.1 ms) lowly-populated (~0.4%) conformational state in HIV-1 TAR RNA. Through a strand register shift, the excited conformational state completely remodels the 3D structure of the ground state (RMSD from the ground state = 7.2 ± 0.9 Å), forming a surprisingly more ordered conformational ensemble rich in non-canonical mismatches. The structure impedes the formation of the motifs recognized by Tat and the super elongation complex, explaining why this alternative TAR conformation cannot activate HIV-1 transcription. The ability to determine the 3D structures of fleeting RNA states using the presented methodology holds great promise for our understanding of RNA biology, disease mechanisms, and the development of RNA-targeting therapeutics.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

Hepatitis B Virus Epsilon (ε) RNA Element: Dynamic Regulator of Viral Replication and Attractive Therapeutic Target

Hepatitis B virus (HBV) chronically infects millions of people worldwide, which underscores the importance of discovering and designing novel anti-HBV therapeutics to complement current treatment strategies. An underexploited but attractive therapeutic target is ε, a cis-acting regulatory stem-loop RNA situated within the HBV pregenomic RNA (pgRNA). The binding of ε to the viral polymerase protein (P) is pivotal, as it triggers the packaging of pgRNA and P, as well as the reverse transcription of the viral genome. Consequently, small molecules capable of disrupting this interaction hold the potential to inhibit the early stages of HBV replication. The rational design of such ligands necessitates high-resolution structural information for the ε-P complex or its individual components. While these data are currently unavailable for P, our recent structural elucidation of ε through solution nuclear magnetic resonance spectroscopy marks a significant advancement in this area. In this review, we provide a brief overview of HBV replication and some of the therapeutic strategies to combat chronic HBV infection. These descriptions are intended to contextualize our recent experimental efforts to characterize ε and identify ε-targeting ligands, with the ultimate goal of developing novel anti-HBV therapeutics.

Small Molecules Targeting Viral RNA

The majority of antivirals available target viral proteins; however, RNA is emerging as a new and promising antiviral target due to the presence of highly structured RNA in viral genomes fundamental for their replication cycle. Here, we discuss methods for the identification of RNA-targeting compounds, starting from the determination of RNA structures either from purified RNA or in living cells, followed by in silico screening on RNA and phenotypic assays to evaluate viral inhibition. Moreover, we review the small molecules known to target the programmed ribosomal frameshifting element of SARS-CoV-2, the internal ribosomal entry site of different viruses, and RNA elements of HIV.

Transcriptome-Wide Studies of RNA-Targeted Small Molecules Provide a Simple and Selective r(CUG)exp Degrader in Myotonic Dystrophy

Myotonic dystrophy type 1 (DM1) is caused by a highly structured RNA repeat expansion, r(CUG)exp, harbored in the 3' untranslated region (3' UTR) of dystrophia myotonica protein kinase (DMPK) mRNA and drives disease through a gain-of-function mechanism. A panel of low-molecular-weight fragments capable of reacting with RNA upon UV irradiation was studied for cross-linking to r(CUG)expin vitro, affording perimidin-2-amine diazirine (1) that bound to r(CUG)exp. The interactions between the small molecule and RNA were further studied by nuclear magnetic resonance (NMR) spectroscopy and molecular modeling. Binding of 1 in DM1 myotubes was profiled transcriptome-wide, identifying 12 transcripts including DMPK that were bound by 1. Augmenting the functionality of 1 with cleaving capability created a chimeric degrader that specifically targets r(CUG)exp for elimination. The degrader broadly improved DM1-associated defects as assessed by RNA-seq, while having limited effects on healthy myotubes. This study (i) provides a platform to investigate molecular recognition of ligands directly in disease-affected cells; (ii) illustrates that RNA degraders can be more specific than the binders from which they are derived; and (iii) suggests that repeating transcripts can be selectively degraded due to the presence of multiple ligand binding sites.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Virtual Screening of Hepatitis B Virus Pre-Genomic RNA as a Novel Therapeutic Target

The global burden imposed by hepatitis B virus (HBV) infection necessitates the discovery and design of novel antiviral drugs to complement existing treatments. One attractive and underexploited therapeutic target is ε, an ~85-nucleotide (nt) cis-acting regulatory stem-loop RNA located at the 3'- and 5'-ends of the pre-genomic RNA (pgRNA). Binding of the 5'-end ε to the viral polymerase protein (P) triggers two early events in HBV replication: pgRNA and P packaging and reverse transcription. Our recent solution nuclear magnetic resonance spectroscopy structure of ε permits structure-informed drug discovery efforts that are currently lacking for P. Here, we employ a virtual screen against ε using a Food and Drug Administration (FDA)-approved compound library, followed by in vitro binding assays. This approach revealed that the anti-hepatitis C virus drug Daclatasvir is a selective ε-targeting ligand. Additional molecular dynamics simulations demonstrated that Daclatasvir targets ε at its flexible 6-nt priming loop (PL) bulge and modulates its dynamics. Given the functional importance of the PL, our work supports the notion that targeting ε dynamics may be an effective anti-HBV therapeutic strategy.

Integrating experimental data with molecular simulations to investigate RNA structural dynamics

Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule Förster resonance energy transfer, or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.

RLBind: a deep learning method to predict RNA-ligand binding sites

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA-small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA-small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA-small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA-small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model's predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.

An overview of structural approaches to study therapeutic RNAs

RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.

Method for Identifying Sequence Motifs in Pre-miRNAs for Small-Molecule Binding

Non-coding RNAs are emerging targets for drug development because they are involved in various cellular processes. However, there are a few reliable design strategies for small molecules that can target RNAs. This paper reports a simple and efficient method to comprehensively analyze RNA motifs that can be bound by a specific small molecule. The method involves Dicer-mediated pre-miRNA cleavage and subsequent analysis of the reaction products by high-throughput sequencing. A pre-miRNA mutant library containing a randomized region at the Dicer cleavage site was used as the substrate for the reaction. Sequencing analysis of the products of the reaction carried out in the presence or absence of a synthetic small molecule identified the pre-miRNA mutants whose Dicer-mediated cleavage was significantly altered by the addition of the small molecule. The binding of the small molecule to the identified pre-miRNA mutants was confirmed by surface plasmon resonance, demonstrating the feasibility of our method.

Targeting RNA structures with small molecules

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.

Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ1-Riboswitch

Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ₁-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.

fingeRNAt—A novel tool for high-throughput analysis of nucleic acid-ligand interactions

Computational methods play a pivotal role in drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the last decades, almost all the attention in medicinal chemistry has been directed to protein-ligand binding, and computational tools have been created with this target in mind. With novel discoveries of functional RNAs and their possible applications, RNAs have gained considerable attention as potential drug targets. However, the availability of bioinformatics tools for nucleic acids is limited. Here, we introduce fingeRNAt—a software tool for detecting non-covalent interactions formed in complexes of nucleic acids with ligands. The program detects nine types of interactions: (i) hydrogen and (ii) halogen bonds, (iii) cation-anion, (iv) pi-cation, (v) pi-anion, (vi) pi-stacking, (vii) inorganic ion-mediated, (viii) water-mediated, and (ix) lipophilic interactions. However, the scope of detected interactions can be easily expanded using a simple plugin system. In addition, detected interactions can be visualized using the associated PyMOL plugin, which facilitates the analysis of medium-throughput molecular complexes. Interactions are also encoded and stored as a bioinformatics-friendly Structural Interaction Fingerprint (SIFt)—a binary string where the respective bit in the fingerprint is set to 1 if a particular interaction is present and to 0 otherwise. This output format, in turn, enables high-throughput analysis of interaction data using data analysis techniques. We present applications of fingeRNAt-generated interaction fingerprints for visual and computational analysis of RNA-ligand complexes, including analysis of interactions formed in experimentally determined RNA-small molecule ligand complexes deposited in the Protein Data Bank. We propose interaction fingerprint-based similarity as an alternative measure to RMSD to recapitulate complexes with similar interactions but different folding. We present an application of interaction fingerprints for the clustering of molecular complexes. This approach can be used to group ligands that form similar binding networks and thus have similar biological properties. The fingeRNAt software is freely available at https://github.com/n-szulc/fingeRNAt.

RNA-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

RLDOCK method for predicting RNA-small molecule binding modes

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

A drug discovery toolbox for Nuclear Magnetic Resonance (NMR) characterization of ligands and their targets

Information about the structure, dynamics, and ligand-binding properties of biomolecules can be derived from Nuclear Magnetic Resonance (NMR) spectroscopy and provides valuable information for drug discovery. A multitude of experimental approaches provides a wealth of information that can be tailored to the system of interest. Methods to study the behavior of ligands upon target binding enable the identification of weak binders in a robust manner that is critical for the identification of truly novel binding interactions. This is particularly important for challenging targets. Observing the solution behavior of biomolecules yields information about their structure, dynamics, and interactions. This review describes the breadth of approaches that are available, many of which are under-utilized in a drug-discovery environment, and focuses on recent advances that continue to emerge.

Cyclic peptides with a distinct arginine-fork motif recognize the HIV trans-activation response RNA in vitro and in cells

RNA represents a potential target for new antiviral therapies, which are urgently needed to address public health threats such as the human immunodeficiency virus (HIV). We showed previously that the interaction between the viral Tat protein and the HIV-1 trans-activation response (TAR) RNA was blocked by the cyclic peptide TB-CP-6.9a. This peptide was derived from a TAR-binding loop that emerged during lab-evolution of a TAR-binding protein (TBP) family. Here we synthesized and characterized a next-generation, cyclic-peptide library based on the TBP scaffold. We sought to identify conserved RNA-binding interactions, and the influence of cyclization linkers on RNA binding and antiviral activity. A diverse group of cyclization linkers, encompassing disulfide bonds to bicyclic aromatic staples, was used to restrain the cyclic peptide geometry. Thermodynamic profiling revealed specific arginine-rich sequences with low to sub-micromolar affinity driven by enthalpic and entropic contributions. The best compounds exhibited no appreciable off-target binding to related molecules, such as BIV TAR and human 7SK RNAs. A specific arginine-to-lysine change in the highest affinity cyclic peptide reduced TAR binding by 10-fold, suggesting that TBP-derived cyclic peptides use an arginine-fork motif to recognize the TAR major-groove while differentiating the mode of binding from other TAR-targeting molecules. Finally, we showed that HIV infectivity in cell culture was reduced in the presence of cyclic peptides constrained by methylene or naphthalene-based linkers. Our findings provide insight into the molecular determinants required for HIV-1 TAR recognition and antiviral activity. These findings are broadly relevant to the development of antivirals that target RNA molecules.

In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19

The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial.

Role of conformational heterogeneity in ligand recognition by viral RNA molecules

Ribonucleic acid (RNA) molecules are known to undergo conformational changes in response to various environmental stimuli including temperature, pH, and ligands. In particular, viral RNA molecules are a key example of conformationally adapting molecules that have evolved to switch between many functional conformations. The transactivation response element (TAR) RNA from the type-1 human immunodeficiency virus (HIV-1) is a viral RNA molecule that is being increasingly explored as a potential therapeutic target due to its role in the viral replication process. In this work, we have studied the dynamics in TAR RNA in apo and liganded states by performing explicit-solvent molecular dynamics (MD) simulations initiated with 27 distinct structures. We determined that the TAR RNA structure is significantly stabilized on ligand binding with especially decreased fluctuations in its two helices. This rigidity is further coupled with the decreased flipping of bulge nucleotides, which were observed to flip more frequently in the absence of ligands. We found that initially-distinct structures of TAR RNA converged to similar conformations on removing ligands. We also report that conformational dynamics in unliganded TAR structures leads to the formation of binding pockets capable of accommodating ligands of various sizes.

Targeting RNA structures in diseases with small molecules

RNA is crucial for gene expression and regulation. Recent advances in understanding of RNA biochemistry, structure and molecular biology have revealed the importance of RNA structure in cellular processes and diseases. Various approaches to discovering drug-like small molecules that target RNA structure have been developed. This review provides a brief introduction to RNA structural biology and how RNA structures function as disease regulators. We summarize approaches to targeting RNA with small molecules and highlight their advantages, shortcomings and therapeutic potential.

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.

Mining for Ligandable Cavities in RNA

Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.

Affecting RNA biology genome-wide by binding small molecules and chemically induced proximity

The ENCODE and genome-wide association projects have shown that much of the genome is transcribed into RNA and much less is translated into protein. These and other functional studies suggest that the druggable transcriptome is much larger than the druggable proteome. This review highlights approaches to define druggable RNA targets and structure-activity relationships across genomic RNA. Binding compounds can be identified and optimized into structure-specific ligands by using sequence-based design with various modes of action, for example, inhibiting translation or directing pre-mRNA splicing outcomes. In addition, strategies to direct protein activity against an RNA of interest via chemically induced proximity is a burgeoning area that has been validated both in cells and in preclinical animal models, and we describe that it may allow rapid access to new avenues to affect RNA biology. These approaches and the unique modes of action suggest that more RNAs are potentially amenable to targeting than proteins.

Developments in solution-state NMR yield broader and deeper views of the dynamic ensembles of nucleic acids

Nucleic acids do not fold into a single conformation, and dynamic ensembles are needed to describe their propensities to cycle between different conformations when performing cellular functions. We review recent advances in solution-state nuclear magnetic resonance (NMR) methods and their integration with computational techniques that are improving the ability to probe the dynamic ensembles of DNA and RNA. These include computational approaches for predicting chemical shifts from structure and generating conformational libraries from sequence, measurements of exact nuclear Overhauser effects, development of new probes to study chemical exchange using relaxation dispersion, faster and more sensitive real-time NMR techniques, and new NMR approaches to tackle large nucleic acid assemblies. We discuss how these advances are leading to new mechanistic insights into gene expression and regulation.

Probing RNA Conformational Equilibria within the Functional Cellular Context

Low-abundance short-lived non-native conformations referred to as excited states (ESs) are increasingly observed in vitro and implicated in the folding and biological activities of regulatory RNAs. We developed an approach for assessing the relative abundance of RNA ESs within the functional cellular context. Nuclear magnetic resonance (NMR) spectroscopy was used to estimate the degree to which substitution mutations bias conformational equilibria toward the inactive ES in vitro. The cellular activity of the ES-stabilizing mutants was used as an indirect measure of the conformational equilibria within the functional cellular context. Compensatory mutations that restore the ground-state conformation were used to control for changes in sequence. Using this approach, we show that the ESs of two regulatory RNAs from HIV-1, the transactivation response element (TAR) and the Rev response element (RRE), likely form in cells with abundances comparable to those measured in vitro, and their targeted stabilization may provide an avenue for developing anti-HIV therapeutics.

Understanding the characteristics of nonspecific binding of drug-like compounds to canonical stem-loop RNAs and their implications for functional cellular assays

Identifying small molecules that selectively bind an RNA target while discriminating against all other cellular RNAs is an important challenge in RNA-targeted drug discovery. Much effort has been directed toward identifying drug-like small molecules that minimize electrostatic and stacking interactions that lead to nonspecific binding of aminoglycosides and intercalators to many stem-loop RNAs. Many such compounds have been reported to bind RNAs and inhibit their cellular activities. However, target engagement and cellular selectivity assays are not routinely performed, and it is often unclear whether functional activity directly results from specific binding to the target RNA. Here, we examined the propensities of three drug-like compounds, previously shown to bind and inhibit the cellular activities of distinct stem-loop RNAs, to bind and inhibit the cellular activities of two unrelated HIV-1 stem-loop RNAs: the transactivation response element (TAR) and the rev response element stem IIB (RREIIB). All compounds bound TAR and RREIIB in vitro, and two inhibited TAR-dependent transactivation and RRE-dependent viral export in cell-based assays while also exhibiting off-target interactions consistent with nonspecific activity. A survey of X-ray and NMR structures of RNA-small molecule complexes revealed that aminoglycosides and drug-like molecules form hydrogen bonds with functional groups commonly accessible in canonical stem-loop RNA motifs, in contrast to ligands that specifically bind riboswitches. Our results demonstrate that drug-like molecules can nonspecifically bind stem-loop RNAs most likely through hydrogen bonding and electrostatic interactions and reinforce the importance of assaying for off-target interactions and RNA selectivity in vitro and in cells when assessing novel RNA-binders.

Frameworks for targeting RNA with small molecules

Since the characterization of mRNA in 1961, our understanding of the roles of RNA molecules has significantly grown. Beyond serving as a link between DNA and proteins, RNA molecules play direct effector roles by binding to various ligands, including proteins, DNA, other RNAs, and metabolites. Through these interactions, RNAs mediate cellular processes such as the regulation of gene transcription and the enhancement or inhibition of protein activity. As a result, the misregulation of RNA molecules is often associated with disease phenotypes, and RNA molecules have been increasingly recognized as potential targets for drug development efforts, which in the past had focused primarily on proteins. Although both small molecule-based and oligonucleotide-based therapies have been pursued in efforts to target RNA, small-molecule modalities are often favored owing to several advantages including greater oral bioavailability. In this review, we discuss three general frameworks (sets of premises and hypotheses) that, in our view, have so far dominated the discovery of small-molecule ligands for RNA. We highlight the unique merits of each framework as well as the pitfalls associated with exclusive focus of ligand discovery efforts within only one framework. Finally, we propose that RNA ligand discovery can benefit from using progress made within these three frameworks to move toward a paradigm that formulates RNA-targeting questions at the level of RNA structural subclasses.

Drug Repurposing Approach against Novel Coronavirus Disease (COVID-19) through Virtual Screening Targeting SARS-CoV-2 Main Protease

Simple Summary

With the urgent necessity of potential treatment against novel coronavirus disease, we used several computational methods to search for active drugs from an extensive database. The results of our investigation suggested several established drugs that can be subjected to further analysis for the treatment of novel coronavirus disease. Various methods used in this study proved the effectiveness of the retrieved drugs. Therefore, our findings highly recommend the mentioned drugs to be scrutinized to discover drugs against novel coronavirus.

Abstract

Novel coronavirus disease (COVID-19) was identified from China in December 2019 and spread rapidly through human-to-human transmission, affecting so many people worldwide. Until now, there has been no specific treatment against the disease and repurposing of the drug. Our investigation aimed to screen potential inhibitors against coronavirus for the repurposing of drugs. Our study analyzed sequence comparison among SARS-CoV, SARS-CoV-2, and MERS-CoV to determine the identity matrix using discovery studio. SARS-CoV-2 M^pro was targeted to generate an E-pharmacophore hypothesis to screen drugs from the DrugBank database having similar features. Promising drugs were used for docking-based virtual screening at several precisions. Best hits from virtual screening were subjected to MM/GBSA analysis to evaluate binding free energy, followed by the analysis of binding interactions. Furthermore, the molecular dynamics simulation approaches were carried out to assess the docked complex’s conformational stability. A total of 33 drug classes were found from virtual screening based on their docking scores. Among them, seven potential drugs with several anticancer, antibiotic, and immunometabolic categories were screened and showed promising MM/GBSA scores. During interaction analysis, these drugs exhibited different types of hydrogen and hydrophobic interactions with amino acid residue. Besides, 17 experimental drugs selected from virtual screening might be crucial for drug discovery against COVID-19. The RMSD, RMSF, SASA, Rg, and MM/PBSA descriptors from molecular dynamics simulation confirmed the complex’s firm nature. Seven promising drugs for repurposing against SARS-CoV-2 main protease (M^pro), namely sapanisertib, ornidazole, napabucasin, lenalidomide, daniquidone, indoximod, and salicylamide, could be vital for the treatment of COVID-19. However, extensive in vivo and in vitro studies are required to evaluate the mentioned drug’s activity.

Small molecule-RNA targeting: starting with the fundamentals

The structural and regulatory elements in therapeutically relevant RNAs offer many opportunities for targeting by small molecules, yet fundamental understanding of what drives selectivity in small molecule:RNA recognition has been a recurrent challenge. In particular, RNAs tend to be more dynamic and offer less chemical functionality than proteins, and biologically active ligands must compete with the highly abundant and highly structured RNA of the ribosome. Indeed, the only small molecule drug targeting RNA other than the ribosome was just approved in August 2020, and our recent survey of the literature revealed fewer than 150 reported chemical probes that target non-ribosomal RNA in biological systems. This Feature outlines our efforts to improve small molecule targeting strategies and gain fundamental insights into small molecule:RNA recognition by analyzing patterns in both RNA-biased small molecule chemical space and RNA topological space privileged for differentiation. First, we synthesized libraries based on RNA binding scaffolds that allowed us to reveal general principles in small molecule:recognition and to ask precise chemical questions about drivers of affinity and selectivity. Elaboration of these scaffolds has led to recognition of medicinally relevant RNA targets, including viral and long noncoding RNA structures. More globally, we identified physicochemical, structural, and spatial properties of biologically active RNA ligands that are distinct from those of protein-targeted ligands, and we have provided the dataset and associated analytical tools as part of a publicly available online platform to facilitate RNA ligand discovery. At the same time, we used pattern recognition protocols to identify RNA topologies that can be differentially recognized by small molecules and have elaborated this technique to visualize conformational changes in RNA secondary structure. These fundamental insights into the drivers of RNA recognition in vitro have led to functional targeting of RNA structures in biological systems. We hope that these initial guiding principles, as well as the approaches and assays developed in their pursuit, will enable rapid progress toward the development of RNA-targeted chemical probes and ultimately new therapeutic approaches to a wide range of deadly human diseases.

Template-guided selection of RNA ligands using imine-based dynamic combinatorial chemistry

This study establishes the applicability of imine-based dynamic combinatorial chemistry to discover non-covalent ligands for RNA targets. We elucidate properties underlying the reactivity of arylamines and demonstrate target-guided amplification of tight binders in an amiloride-based dynamic library.

RNA Drugs and RNA Targets for Small Molecules: Principles, Progress, and Challenges

RNA-based therapies, including RNA molecules as drugs and RNA-targeted small molecules, offer unique opportunities to expand the range of therapeutic targets. Various forms of RNAs may be used to selectively act on proteins, transcripts, and genes that cannot be targeted by conventional small molecules or proteins. Although development of RNA drugs faces unparalleled challenges, many strategies have been developed to improve RNA metabolic stability and intracellular delivery. A number of RNA drugs have been approved for medical use, including aptamers (e.g., pegaptanib) that mechanistically act on protein target and small interfering RNAs (e.g., patisiran and givosiran) and antisense oligonucleotides (e.g., inotersen and golodirsen) that directly interfere with RNA targets. Furthermore, guide RNAs are essential components of novel gene editing modalities, and mRNA therapeutics are under development for protein replacement therapy or vaccination, including those against unprecedented severe acute respiratory syndrome coronavirus pandemic. Moreover, functional RNAs or RNA motifs are highly structured to form binding pockets or clefts that are accessible by small molecules. Many natural, semisynthetic, or synthetic antibiotics (e.g., aminoglycosides, tetracyclines, macrolides, oxazolidinones, and phenicols) can directly bind to ribosomal RNAs to achieve the inhibition of bacterial infections. Therefore, there is growing interest in developing RNA-targeted small-molecule drugs amenable to oral administration, and some (e.g., risdiplam and branaplam) have entered clinical trials. Here, we review the pharmacology of novel RNA drugs and RNA-targeted small-molecule medications, with a focus on recent progresses and strategies. Challenges in the development of novel druggable RNA entities and identification of viable RNA targets and selective small-molecule binders are discussed. SIGNIFICANCE STATEMENT: With the understanding of RNA functions and critical roles in diseases, as well as the development of RNA-related technologies, there is growing interest in developing novel RNA-based therapeutics. This comprehensive review presents pharmacology of both RNA drugs and RNA-targeted small-molecule medications, focusing on novel mechanisms of action, the most recent progress, and existing challenges.

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 ¹⁹F 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.

Target-Directed Approaches for Screening Small Molecules against RNA Targets

RNA molecules have a variety of cellular functions that can drive disease pathologies. They are without a doubt one of the most intriguing yet controversial small-molecule drug targets. The ability to widely target RNA with small molecules could be revolutionary, once the right tools, assays, and targets are selected, thereby defining which biomolecules are targetable and what constitutes drug-like small molecules. Indeed, approaches developed over the past 5-10 years have changed the face of small molecule-RNA targeting by addressing historic concerns regarding affinity, selectivity, and structural dynamics. Presently, selective RNA-protein complex stabilizing drugs such as branaplam and risdiplam are in clinical trials for the modulation of SMN2 splicing, compounds identified from phenotypic screens with serendipitous outcomes. Fully developing RNA as a druggable target will require a target engagement-driven approach, and evolving chemical collections will be important for the industrial development of this class of target. In this review we discuss target-directed approaches that can be used to identify RNA-binding compounds and the chemical knowledge we have today of small-molecule RNA binders.