The Emerging Role of RNA as a Therapeutic Target for Small Molecules

Cyclic peptides targeting the SARS-CoV-2 programmed ribosomal frameshifting RNA from a multiplexed phage display library

RNA provides the genetic blueprint for many pathogenic viruses, including SARS-CoV-2. The propensity of RNA to fold into specific tertiary structures enables the biomolecular recognition of cavities and crevices suited for the binding of drug-like molecules. Despite increasing interest in RNA as a target for chemical biology and therapeutic applications, the development of molecules that recognize RNA with high affinity and specificity represents a significant challenge. Here, we report a strategy for the discovery and selection of RNA-targeted macrocyclic peptides derived from combinatorial libraries of peptide macrocycles displayed by bacteriophages. Specifically, a platform for phage display of macrocyclic organo-peptide hybrids (MOrPH-PhD) was combined with a diverse set of non-canonical amino acid-based cyclization modules to produce large libraries of 107 structurally diverse, genetically encoded peptide macrocycles. These libraries were panned against the -1 programmed ribosomal frameshifting stimulatory sequence (FSS) RNA pseudoknot of SARS-CoV-2, which revealed specific macrocyclic peptide sequences that bind this essential motif with high affinity and selectivity. Peptide binding localizes to the FSS dimerization loop based on chemical modification analysis and binding assays and the cyclic peptides show specificity toward the target RNA over unrelated RNA pseudoknots. This work introduces a novel system for the generation and high-throughput screening of topologically diverse cyclopeptide scaffolds (multiplexed MOrPH-PhD), and it provides a blueprint for the exploration and evolution of genetically encoded macrocyclic peptides that target specific RNAs.

MicroRNA-specific targets for neuronal plasticity, neurotransmitters, neurotrophic factors, and gut microbes in the pathogenesis and therapeutics of depression

Depression is of great concern because of the huge burden, and it is impacted by various epigenetic modifications, e.g., histone modification, covalent modifications in DNA, and silencing mechanisms of non-coding protein genes, e.g., microRNAs (miRNAs). MiRNAs are a class of endogenous non-coding RNAs. Alternations in specific miRNAs have been observed both in depressive patients and experimental animals. Also, miRNAs are highly expressed in the central nervous system and can be delivered to different tissues via tissue-specific exosomes. However, the mechanism of miRNAs' involvement in the pathological process of depression is not well understood. Therefore, we summarized and discussed the role of miRNAs in depression. Conclusively, miRNAs are involved in the pathology of depression by causing structural and functional changes in synapses, mediating neuronal regeneration, differentiation, and apoptosis, regulating the gut microbes and the expression of various neurotransmitters and BDNF, and mediating inflammatory and immune responses. Moreover, miRNAs can predict the efficacy of antidepressant medications and explain the mechanism of action of antidepressant drugs and aerobic exercise to prevent and assist in treating depression.

Mechanistic Studies of Small Molecule Ligands Selective to RNA Single G Bulges

Small-molecule RNA binders have emerged as an important pharmacological modality. A profound understanding of the ligand selectivity, binding mode, and influential factors governing ligand engagement with RNA targets is the foundation for rational ligand design. Here, we report a novel class of coumarin derivatives exhibiting selective binding affinity towards single G RNA bulges. Harnessing the computational power of all-atom Gaussian accelerated Molecular Dynamics (GaMD) simulations, we unveiled a rare minor groove binding mode of the ligand with a key interaction between the coumarin moiety and the G bulge. This predicted binding mode is consistent with results obtained from structure-activity-relationship (SAR) studies and transverse relaxation measurements by NMR spectroscopy. We further generated 444 molecular descriptors from 69 coumarin derivatives and identified key contributors to the binding events, such as charge state and planarity, by lasso (least absolute shrinkage and selection operator) regression. Strikingly, small structure perturbations on these key contributors, such as the addition of a methyl group that disrupts the planarity of the ligand resulted in > 100-fold reduction in the binding affinity. Our work deepened the understanding of RNA-small molecule interactions and integrated a new generalizable platform for the rational design of selective small-molecule RNA binders.

Genome-Wide Identification of Stable RNA Secondary Structures Across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics

Small molecules targeting specific RNA binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA Secondary Structure Finder (R2S-Finder) to discover short, stable RNA structural motifs for humans, Escherichia coli ( E. coli ), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also found that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans, SARS-CoV-2, and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent in humans, E. coli , and SARS-CoV-2, indicating that most stable structure formation were driven by RNA folding alone, while a larger variation was found between in vitro and in vivo data with certain RNA types, such as human long intergenic non-coding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist both in vivo and in vitro conditions, which can potentially serve as drug targets. All results of stable sequences, stem-loops, internal loops, bulges, and three- and four-way junctions have been collated in the R2S-Finder database ( https://github.com/JingxinWangLab/R2S-Finder ), which is coded in hyperlinked HTML pages and tabulated in CSV files.

RNA-Binding Small Molecules in Drug Discovery and Delivery: An Overview from Fundamentals

RNA molecules, similar to proteins, fold into complex structures to confer diverse functions in cells. The intertwining of functions with RNA structures offers a new therapeutic opportunity for small molecules to bind and manipulate disease-relevant RNA pathways, thus creating a therapeutic realm of RNA-binding small molecules. The ongoing interest in RNA targeting and subsequent screening campaigns have led to the identification of numerous compounds that can regulate RNAs from splicing, degradation to malfunctions, with therapeutic benefits for a variety of diseases. Moreover, along with the rise of RNA-based therapeutics, RNA-binding small molecules have expanded their application to the modification, regulation, and delivery of RNA drugs, leading to the burgeoning interest in this field. This Perspective overviews the emerging roles of RNA-binding small molecules in drug discovery and delivery, covering aspects from their action fundamentals to therapeutic applications, which may inspire researchers to advance the field.

Disulfide Tethering to Map Small Molecule Binding Sites Transcriptome-wide

We report the development of Tether-seq, a transcriptome-wide screen to probe RNA-small molecule interactions using disulfide tethering. This technique uses s4U metabolic labeling to provide sites for reversible and covalent attachment of small molecule disulfides to the transcriptome. By screening under reducing conditions, we identify interactions that are stabilized by binding over those driven by the reactivity of the RNA sites. When applied to cellular RNA, Tether-seq with a disulfide analogue of risdiplam, an FDA-approved drug that targets RNA to treat spinal muscular atrophy (SMA), revealed a number of potential binding sites, most prominently at a site within the cytochrome C oxidase 1 (COX1) transcript. Structure probing by SHAPE-MaP revealed a structured motif and confirmed binding to the lead molecule. This work demonstrates that these screens have the power to identify binding sites throughout the transcriptome and provide invaluable insight into the thermodynamic properties that define small molecule binding.

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTP riboswitch ligands

The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure-informed design, crystallography, and machine learning-augmented all-atom molecular dynamics simulations (MD) we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the ZTP riboswitch, a ligand-binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on-rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Characterization of the SARS-CoV-2 Genome 3'-Untranslated Region Interactions with Host MicroRNAs

The 2019 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has marked the spread of a novel human coronavirus. SARS-CoV-2 has exhibited increased disease severity and immune evasion across its variants, and the molecular mechanisms behind these phenomena remain largely unknown. Conserved elements of the viral genome, such as secondary structures within the 3'-untranslated region (UTR), could prove crucial in furthering our understanding of the host-virus interface. Analysis of the SARS-CoV-2 viral genome 3'-UTR revealed the potential for host microRNA (miR) binding sites, allowing for sequence-specific interactions. In this study, we demonstrate that the SARS-CoV-2 genome 3'-UTR binds the host cellular miRs miR-34a-5p, miR-34b-5p, and miR-760-3p in vitro. Native gel electrophoresis and steady-state fluorescence spectroscopy were utilized to biophysically characterize the binding of these miRs to their predicted sites within the SARS-CoV-2 genome 3'-UTR. Additionally, we investigated 2'-fluoro-d-arabinonucleic acid (FANA) analogs as competitive binding inhibitors for these interactions. These miRs modulate the translation of granulin (GRN), interleukin-6 (IL-6), and the IL-6 receptor (IL-6R), all of which are key modulators and activators of JAK/STAT3 signaling and are implicated in regulation of the immune response. Thus, we propose that hijacking of these miRs by SARS-CoV-2 could identify a mechanism of host immune modulation by the virus. The mechanisms detailed in this study have the potential to drive the development of antiviral treatments for SARS-CoV-2, through direct targeting of the virus-host interface.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Investigating the Intramolecular Competition of Different RNA Binding Motifs for Neomycin B by Native Top-Down Mass Spectrometry

The ongoing search for small molecule drugs that target ribonucleic acids (RNA) is complicated by a limited understanding of the principles that govern RNA-small molecule interactions. Here we have used stoichiometry-resolved native top-down mass spectrometry (MS) to study the binding of neomycin B to small model hairpin RNAs, an unstructured RNA, and a viral RNA construct. For 15-22 nt model RNAs with hairpin structure, we found that neomycin B binding to hairpin loops relies on interactions with both the nucleobases and the 2'-OH groups, and that a simple 5' or 3' overhang can introduce an additional binding motif. For a 47 nt RNA construct derived from stem IA of the human immunodeficiency virus 1 (HIV-1) rev response element (RRE) RNA, native top-down MS identified four different binding motifs, of which the purine-rich internal loop showed the highest affinity for neomycin B. Stoichiometry-resolved binding site mapping by native top-down MS allows for a new perspective on binding specificity, and has the potential to reveal unexpected principles of small molecule binding to RNA.

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

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.

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.

RNP granules in ALS and neurodegeneration: From multifunctional membraneless organelles to therapeutic opportunities

Amyotrophic lateral sclerosis (ALS) and related neurodegenerative diseases are characterised by dysfunction of a host of RNA-binding proteins (RBPs) and a severely disrupted RNA metabolism. Recently, RBP-harbouring phase-separated complexes, ribonucleoprotein (RNP) granules, have come into the limelight as "crucibles" of neuronal pathology in ALS. RNP granules are indispensable for the multitude of regulatory processes underlying cellular RNA metabolism and serve as critical organisers of cellular biochemistry. Neurons, highly specialised cells, heavily rely on RNP granules for efficient trafficking, signalling and stress responses. Multiple RNP granule components, primarily RBPs such as TDP-43 and FUS, are affected by ALS mutations. However, even in the absence of mutations, RBP proteinopathies represent pathophysiological hallmarks of ALS. Given the high local concentrations of RBPs and RNAs, their weakened or enhanced interactions within RNP granules disrupt their homeostasis. Thus, the physiological process of phase separation and RNP granule formation, vital for maintaining the high-functioning state of neuronal cells, becomes their Achilles heel. Here, we will review the recent literature on the causes and consequences of abnormal RNP granule functioning in ALS and related disorders. In particular, we will summarise the evidence for the network-level dysfunction of RNP granules in these conditions and discuss considerations for therapeutic interventions to target RBPs, RNP granules and their network as a whole.

Interaction of 3,9-disubstituted acridine with single stranded poly(rA), double stranded poly(rAU) and triple stranded poly(rUAU): molecular docking - A spectroscopic tandem study

RNA plays an important role in many biological processes which are crucial for cell survival, and it has been suggested that it may be possible to inhibit individual processes involved in many diseases by targeting specific sequences of RNA. The aim of this work is to determine the affinity of novel 3,9-disubstited acridine derivative 1 with three different RNA molecules, namely single stranded poly(rA), double stranded homopolymer poly(rAU) and triple stranded poly(rUAU). The results of the absorption titration assays show that the binding constant of the novel derivative to the RNA molecules was in the range of 1.7-6.2 × 104 mol dm-3. The fluorescence and circular dichroism titration assays revealed considerable changes. The most significant results in terms of interpreting the nature of the interactions were the melting temperatures of the RNA samples in complexes with the 1. In the case of poly(rA), denaturation resulted in a self-structure formation; increased stabilization was observed for poly(rAU), while the melting points of the ligand-poly(rUAU) complex showed significant destabilization as a result of the interaction. The principles of molecular mechanics were applied to propose the non-bonded interactions within the binding complex, pentariboadenylic acid and acridine ligand as the study model. Initial molecular docking provided the input structure for advanced simulation techniques. Molecular dynamics simulation and cluster analysis reveal π - π stacking and the hydrogen bonds formation as the main forces that can stabilize the binding complex. Subsequent MM-GBSA calculations showed negative binding enthalpy accompanied the complex formation and proposed the most preferred conformation of the interaction complex.

Small molecules modulating RNA splicing: a review of targets and future perspectives

In eukaryotic cells, RNA splicing is crucial for gene expression. Dysregulation of this process can result in incorrect mRNA processing, leading to aberrant gene expression patterns. Such abnormalities are implicated in many inherited diseases and cancers. Historically, antisense oligonucleotides, which bind to specific RNA targets, have been used to correct these splicing abnormalities. Despite their high specificity of action, these oligonucleotides have drawbacks, such as lack of oral bioavailability and the need for chemical modifications to enhance cellular uptake and stability. As a result, recent efforts focused on the development of small organic molecules that can correct abnormal RNA splicing event under disease conditions. This review discusses known and potential targets of these molecules, including RNA structures, trans-acting splicing factors, and the spliceosome - the macromolecular complex responsible for RNA splicing. We also rely on recent advances to discuss therapeutic applications of RNA-targeting small molecules in splicing correction. Overall, this review presents an update on strategies for RNA splicing modulation, emphasizing the therapeutic promise of small molecules.

Aminothiazolone Inhibitors Disrupt the Protein-RNA Interaction of METTL16 and Modulate the m6A RNA Modification

Targeting RNA-binding and modifying proteins via small molecules to modulate post-transcriptional modifications have emerged as a new frontier for chemical biology and therapeutic research. One such RNA-binding protein that regulates the most prevalent eukaryotic RNA modification, N6-methyladenosine (m6A), is the methyltransferase-like protein 16 (METTL16), which plays an oncogenic role in cancers by cofunctioning with other nucleic acid-binding proteins. To date, no potent small-molecule inhibitor of METTL16 or modulator interfering with the METTL16-RNA interaction has been reported and validated, highlighting the unmet need to develop such small molecules to investigate the METTL16-involved regulatory network. Herein, we described the identification of a series of first-in-class aminothiazolone METTL16 inhibitors via a discovery pipeline that started with a fluorescence-polarization (FP)-based screening. Structural optimization of the initial hit yielded inhibitors, such as compound 45, that showed potent single-digit micromolar inhibition activity against the METTL16-RNA binding. The identified aminothiazolone inhibitors can be useful probes to elucidate the biological function of METTL16 upon perturbation and evaluate the therapeutic potential of METTL16 inhibition via small molecules at the post-transcriptional level.

RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag

RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

DNA methylation induces subtle mechanical alteration but significant chiral selectivity

DNA methylation is a major epigenetic modification that is closely related to human health. Many experimental techniques as well as theoretical methods have been used to detect the modified nucleotides and identify their effects on molecular binding. It remains challenging to resolve the effect of few methylations of nucleic acids. Using super-resolution force spectroscopy, we firstly revealed that single cytosine methylation increases the mechanical stability of the DNA duplex by 1.9 ± 0.3 pN. Methylation also induces significant chiral selectivity towards drug molecules such as d,l-tetrahydropalmatine. Our results precisely quantify the mechanical effect of methylation and suggest that drug design should take methylation into consideration for enhanced selectivity.

Photoswitchable molecular glue for RNA: reversible photocontrol of structure and function of the ribozyme

Single-stranded RNA folds into a variety of secondary and higher-order structures. Distributions and dynamics of multiple RNA conformations are responsible for the biological function of RNA. We here developed a photoswitchable molecular glue for RNA, which could reversibly control the association of two unpaired RNA regions in response to light stimuli. The photoswitchable molecular glue, NCTA, is an RNA-binding ligand possessing a photoisomerizable azobenzene moiety. Z-NCTA is an active ligand for the target RNA containing 5'-WGG-3'/5'-WGG-3' (W = U or A) site and stabilizes its hybridized state, while its isomer E-NCTA is not. Photoreversible isomerization of NCTA enabled control of the secondary and tertiary structure of the target RNA. The RNA-cleaving activity of hammerhead ribozyme, where appropriate RNA folding is necessary, could be reversibly regulated by photoirradiation in cells treated with NCTA, demonstrating precise photocontrol of RNA structure and function by the photoswitchable molecular glue.

RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery

RNA structures, including those formed from coding and noncoding RNAs, alternative to protein-based drug targets, could be a promising target of small molecules for drug discovery against various human diseases, particularly in anticancer, antibacterial and antivirus development. The normal cellular activity of cells is critically dependent on the function of various RNA molecules generated from DNA transcription. Moreover, many studies support that mRNA-targeting small molecules may regulate the synthesis of disease-related proteins via the non-covalent mRNA-ligand interactions that do not involve gene modification. RNA-ligand interaction is thus an attractive approach to address the challenge of "undruggable" proteins in drug discovery because the intracellular activity of these proteins is hard to be suppressed with small molecule ligands. We selectively surveyed a specific area of RNA structure-selective small molecule ligands in fluorescence live cell imaging and drug discovery because the area was currently underexplored. This state-of-the-art review thus mainly focuses on the research published within the past three years and aims to provide the most recent information on this research area; hopefully, it could be complementary to the previously reported reviews and give new insights into the future development on RNA-specific small molecule ligands for live cell imaging and drug discovery.

Non-covalent dyes in microscale thermophoresis for studying RNA ligand interactions and modifications

Microscale Thermophoresis (MST) is a powerful biophysical technique that measures the mobility of biomolecules in response to a temperature gradient, making it useful for investigating the interactions between biological molecules. This study presents a novel methodology for studying RNA-containing samples using non-covalent nucleic acid-sensitive dyes in MST. This "mix-and-measure" protocol uses non-covalent dyes, such as those from the Syto or Sybr series, which lead to the statistical binding of one fluorophore per RNA oligo showing key advantages over traditional covalent labelling approaches. This new approach has been successfully used to study the binding of ligands to RNA molecules (e.g., SAM- and PreQ1 riboswitches) and the identification of modifications (e.g., m6A) in short RNA oligos which can be written by the RNA methyltransferase METTL3/14.

Spirocyclic Chromenopyrazole Inhibitors Disrupting the Interaction between the RNA-Binding Protein LIN28 and Let-7

Small-molecule inhibitors of the RNA-binding and regulating protein LIN28 have the potential to be developed as chemical probes for biological perturbation and as therapeutic candidates. Reported small molecules disrupting the interaction between LIN28 and let-7 miRNA suffer from moderate to weak inhibitory activity and flat structure-activity relationship, which hindered the development of next-generation LIN28 inhibitors that warrant further evaluations. We report herein the identification of new LIN28 inhibitors utilizing a spirocyclization strategy based on a chromenopyrazole scaffold. Representative compounds 2-5 showed potent in vitro inhibitory activity against LIN28-let-7 interaction and single-digit micromolar potency in inhibiting the proliferation of LIN28-expressing JAR cancer cells. The spirocyclic compound 5 incorporated a position that is amenable for functional group appendage and further structural modifications. The binding mode of compound 5 with the LIN28 cold shock domain was rationalized via a molecular docking analysis.

Chromenopyrazole-Peptide Conjugates as Small-Molecule Based Inhibitors Disrupting the Protein-RNA Interaction of LIN28-let-7

Targeting the protein-RNA interaction of LIN28 and let-7 is a promising strategy for the development of novel anticancer therapeutics. However, a limited number of small-molecule inhibitors disrupting the LIN28-let-7 interaction with potent efficacy are available. Herein, we developed a novel LIN28-inhibiting strategy by targeting selective hotspot amino acids at the LIN28-let-7 binding interface with small-molecule-based bifunctional conjugates. Starting from reported small-molecule LIN28 inhibitors, we identified a feasible linker-attachment position after performing a structure-activity relationship exploration based on the LIN28-targeting chromenopyrazoles. In parallel, a virtual alanine scan identified hotspot residues at the protein-RNA binding interface, based on which we designed a set of peptides to enhance the interaction with the identified hotspot residues. Conjugation of the tailor-designed peptides with linker-attached chromenopyrazoles yielded a series of bifunctional small-molecule-peptide conjugates, represented by compound 83 (PH-223), as a new LIN28-targeting chemical modality. Our result demonstrated an unexplored rational design approach using bifunctional conjugates to target protein-RNA interactions.

Biopolymer collagen-chitosan scaffold containing Aloe vera for chondrogenic efficacy on cartilage tissue engineering

The chondrogenic efficacy of aloe vera blended collagen-chitosan (COL-CS-AV) porous scaffold was investigated using articular chondrocytes in a standard condition. Cytocompatibility was analyzed using fluorescent dyes (calcein AM/ethidium bromide) and the viable cells were quantified by MTT assay. Glycosaminoglycan (GAG) content of ECM was estimated by using 1, 9-Dimethyl methylene Blue (DMMB). The total RNA content was quantified and the cartilage specific genes (col2a1, Acan) were amplified by reverse transcription-PCR from the cell lysate of the scaffolds. Histological examination was made using Haematoxylin and Eosin (H&E), safranin-O, masson's trichrome, alcian blue, and alizarin red to stain the specific component of ECM secreted on the construct. The cartilage specific collagen type II was estimated by immunohistochemistry using monoclonal type II collagen antibody. The results of these studies proved that COL-CS-AV scaffold has more chondrogenic efficacy than COL-CS, thus the aloe vera blend COL-CS-AV scaffold might be used as suitable candidate for cartilage tissue engineering.

Targeting RNA-binding proteins with small molecules: Perspectives, pitfalls and bifunctional molecules

RNA-binding proteins (RBPs) play vital roles in organisms through binding with RNAs to regulate their functions. Small molecules affecting the function of RBPs have been developed, providing new avenues for drug discovery. Herein, we describe the perspectives on developing small molecule regulators of RBPs. The following types of small molecule modulators are of great interest in drug discovery: small molecules binding to RBPs to affect interactions with RNA molecules, bifunctional molecules binding to RNA or RBP to influence their interactions, and other types of molecules that affect the stability of RNA or RBPs. Moreover, we emphasize that the bifunctional molecules may play important roles in small molecule development to overcome the challenges encountered in the process of drug discovery.

MicroRNA-based therapeutics for inflammatory disorders of the microbiota-gut-brain axis

An emerging but less explored shared pathophysiology across microbiota-gut-brain axis disorders is aberrant miRNA expression, which may represent novel therapeutic targets. miRNAs are small, endogenous non-coding RNAs that are important transcriptional repressors of gene expression. Most importantly, they regulate the integrity of the intestinal epithelial and blood-brain barriers and serve as an important communication channel between the gut microbiome and the host. A well-defined understanding of the mode of action, therapeutic strategies and delivery mechanisms of miRNAs is pivotal in translating the clinical applications of miRNA-based therapeutics. Accumulating evidence links disorders of the microbiota-gut-brain axis with a compromised gut-blood-brain-barrier, causing gut contents such as immune cells and microbiota to enter the bloodstream leading to low-grade systemic inflammation. This has the potential to affect all organs, including the brain, causing central inflammation and the development of neurodegenerative and neuropsychiatric diseases. In this review, we have examined in detail miRNA biogenesis, strategies for therapeutic application, delivery mechanisms, as well as their pathophysiology and clinical applications in inflammatory gut-brain disorders. The research data in this review was drawn from the following databases: PubMed, Google Scholar, and Clinicaltrials.gov. With increasing evidence of the pathophysiological importance for miRNAs in microbiota-gut-brain axis disorders, therapeutic targeting of cross-regulated miRNAs in these disorders displays potentially transformative and translational potential. Further preclinical research and human clinical trials are required to further advance this area of research.

Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening

Nowadays, RNA is an attractive target for the design of new small molecules with different pharmacological activities. Among several RNA molecules, long noncoding RNAs (lncRNAs) are extensively reported to be involved in cancer pathogenesis. In particular, the overexpression of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays an important role in the development of multiple myeloma (MM). Starting from the crystallographic structure of the triple-helical stability element at the 3'-end of MALAT1, we performed a structure-based virtual screening of a large commercial database, previously filtered according to the drug-like properties. After a thermodynamic analysis, we selected five compounds for the in vitro assays. Compound M5, characterized by a diazaindene scaffold, emerged as the most promising molecule enabling the destabilization of the MALAT1 triplex structure and antiproliferative activity on in vitro models of MM. M5 is proposed as a lead compound to be further optimized for improving its affinity toward MALAT1.

Probing Bioactive Chemical Space to Discover RNA-Targeted Small Molecules

Small molecules have become increasingly recognized as invaluable tools to study RNA structure and function and to develop RNA-targeted therapeutics. To rationally design RNA-targeting ligands, a comprehensive understanding and explicit testing of small molecule properties that govern molecular recognition is crucial. To date, most studies have primarily evaluated properties of small molecules that bind RNA in vitro, with little to no assessment of properties that are distinct to selective and bioactive RNA-targeted ligands. Therefore, we curated an RNA-focused library, termed the Duke RNA-Targeted Library (DRTL), that was biased towards the physicochemical and structural properties of biologically active and non-ribosomal RNA-targeted small molecules. The DRTL represents one of the largest academic RNA-focused small molecule libraries curated to date with more than 800 small molecules. These ligands were selected using computational approaches that measure similarity to known bioactive RNA ligands and that diversify the molecules within this space. We evaluated DRTL binding in vitro to a panel of four RNAs using two optimized fluorescent indicator displacement assays, and we successfully identified multiple small molecule hits, including several novel scaffolds for RNA. The DRTL has and will continue to provide insights into biologically relevant RNA chemical space, such as the identification of additional RNA-privileged scaffolds and validation of RNA-privileged molecular features. Future DRTL screening will focus on expanding both the targets and assays used, and we welcome collaboration from the scientific community. We envision that the DRTL will be a valuable resource for the discovery of RNA-targeted chemical probes and therapeutic leads.

Reversible Control of RNA Splicing by Photoswitchable Small Molecules

Dynamics are intrinsic to both RNA function and structure. Yet, the available means to precisely provide RNA-based processes with spatiotemporal resolution are scarce. Here, our work pioneers a reversible approach to regulate RNA splicing within primary patient-derived cells by synthetic photoswitches. Our small molecule enables conditional real-time control at mRNA and protein levels. NMR experiments, together with theoretical calculations, photochemical characterization, fluorescence polarization measurements, and living cell-based assays, confirmed light-dependent exon inclusion as well as an increase in the target functional protein. Therefore, we first demonstrated the potential of photopharmacology modulation in splicing, tweaking the current optochemical toolkit. The timeliness on the consolidation of RNA research as the driving force toward therapeutical innovation holds the promise that our approach will contribute to redrawing the vision of RNA.

Proximity-Induced Nucleic Acid Degrader (PINAD) Approach to Targeted RNA Degradation Using Small Molecules

Nature has evolved intricate machinery to target and degrade RNA, and some of these molecular mechanisms can be adapted for therapeutic use. Small interfering RNAs and RNase H-inducing oligonucleotides have yielded therapeutic agents against diseases that cannot be tackled using protein-centered approaches. Because these therapeutic agents are nucleic acid-based, they have several inherent drawbacks which include poor cellular uptake and stability. Here we report a new approach to target and degrade RNA using small molecules, proximity-induced nucleic acid degrader (PINAD). We have utilized this strategy to design two families of RNA degraders which target two different RNA structures within the genome of SARS-CoV-2: G-quadruplexes and the betacoronaviral pseudoknot. We demonstrate that these novel molecules degrade their targets using in vitro, in cellulo, and in vivo SARS-CoV-2 infection models. Our strategy allows any RNA binding small molecule to be converted into a degrader, empowering RNA binders that are not potent enough to exert a phenotypic effect on their own. PINAD raises the possibility of targeting and destroying any disease-related RNA species, which can greatly expand the space of druggable targets and diseases.

Targeting RNA with small molecules: lessons learned from Xist RNA

Although more than 98% of the human genome is noncoding, nearly all drugs on the market target one of about 700 disease-related proteins. However, an increasing number of diseases are now being attributed to noncoding RNA and the ability to target them would vastly expand the chemical space for drug development. We recently devised a screening strategy based upon affinity-selection mass spectrometry and succeeded in identifying bioactive compounds for the noncoding RNA prototype, Xist. One such compound, termed X1, has drug-like properties and binds specifically to the RepA motif of Xist in vitro and in vivo. Small-angle X-ray scattering analysis reveals that X1 changes the conformation of RepA in solution, thereby explaining the displacement of cognate interacting protein factors (PRC2 and SPEN) and inhibition of X-chromosome inactivation. In this Perspective, we discuss lessons learned from these proof-of-concept experiments and suggest that RNA can be systematically targeted by drug-like compounds to disrupt RNA structure and function.

Innovations in targeting RNA by fragment-based ligand discovery

A subset of functional regions within large RNAs fold into complex structures able to bind small-molecule ligands with high affinity and specificity. Fragment-based ligand discovery (FBLD) offers notable opportunities for discovery and design of potent small molecules that bind pockets in RNA. Here we share an integrated analysis of recent innovations in FBLD, emphasizing opportunities resulting from fragment elaboration via both linking and growing. Analysis of elaborated fragments emphasizes that high-quality interactions form with complex tertiary structures in RNA. FBLD-inspired small molecules have been shown to modulate RNA functions by competitively inhibiting protein binding and by selectively stabilizing dynamic RNA states. FBLD is creating a foundation to interrogate the relatively unknown structural space for RNA ligands and for discovery of RNA-targeted therapeutics.

Aid or Antagonize: Nuclear Long Noncoding RNAs Regulate Host Responses and Outcomes of Viral Infections

Long noncoding RNAs (lncRNAs) are transcripts measuring >200 bp in length and devoid of protein-coding potential. LncRNAs exceed the number of protein-coding mRNAs and regulate cellular, developmental, and immune pathways through diverse molecular mechanisms. In recent years, lncRNAs have emerged as epigenetic regulators with prominent roles in health and disease. Many lncRNAs, either host or virus-encoded, have been implicated in critical cellular defense processes, such as cytokine and antiviral gene expression, the regulation of cell signaling pathways, and the activation of transcription factors. In addition, cellular and viral lncRNAs regulate virus gene expression. Viral infections and associated immune responses alter the expression of host lncRNAs regulating immune responses, host metabolism, and viral replication. The influence of lncRNAs on the pathogenesis and outcomes of viral infections is being widely explored because virus-induced lncRNAs can serve as diagnostic and therapeutic targets. Future studies should focus on thoroughly characterizing lncRNA expressions in virus-infected primary cells, investigating their role in disease prognosis, and developing biologically relevant animal or organoid models to determine their suitability for specific therapeutic targeting. Many cellular and viral lncRNAs localize in the nucleus and epigenetically modulate viral transcription, latency, and host responses to infection. In this review, we provide an overview of the role of nuclear lncRNAs in the pathogenesis and outcomes of viral infections, such as the Influenza A virus, Sendai Virus, Respiratory Syncytial Virus, Hepatitis C virus, Human Immunodeficiency Virus, and Herpes Simplex Virus. We also address significant advances and barriers in characterizing lncRNA function and explore the potential of lncRNAs as therapeutic targets.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

Translating the Genome into Drugs

The Human Genome Project ultimately aimed to translate DNA sequence into drugs. With the draft in hand, the Molecular Libraries Program set out to prosecute all genome-encoded proteins for drug discovery with automated high-throughput screening (HTS). This ambitious vision remains unfulfilled, even while innovations in sequencing technology have fully democratized access to genome-scale sequencing. Why? While the central dogma of biology allows us to chart the entirety of cellular metabolism through sequencing, there is no direct coding for chemistry. The rules of base pairing that relate DNA gene to RNA transcript and amino acid sequence do not exist for relating small-molecule structure with macromolecular binding partners and subsequently cellular function. Obtaining such relationships genome-wide is unapproachable via state-of-the-art HTS, akin to attempting genome-wide association studies using turn-of-the-millennium Sanger DNA sequencing.Our laboratory has been engaged in a multipronged technology development campaign to revolutionize molecular screening through miniaturization in pursuit of genome-scale drug discovery capabilities. The compound library was ripe for miniaturization: it clearly needed to become a consumable. We employed DNA-encoded library (DEL) synthesis principles in the development of solid-phase DELs prepared on microscopic beads, each harboring 100 fmol of a single library member and a DNA tag whose sequence describes the structure of the library member. Loading these DEL beads into 100 pL microfluidic droplets followed by online photocleavage, incubation, fluorescence-activated droplet sorting, and DNA sequencing of the sorted DEL beads reveals the chemical structures of bioactive compounds. This scalable library synthesis and screening platform has proven useful in several proof-of-concept projects involving current clinical targets.Moving forward, we face the problem of druggability and proteome-scale assay development. Developing biochemical or cellular assays for all genome-encoded targets is not scalable and likely impossible as most proteins have ill-defined or unknown activity and may not function outside of their native contexts. These are the dark undruggable expanses, and charting them will require advanced synthesis and analytical technologies that can generalize probe discovery, irrespective of mature protein function, to fulfill the Genome Project's vision of proteome-wide control of cellular pharmacology.

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.

X-Ray Crystallography to Study Conformational Changes in a TPP Riboswitch

Conformational rearrangements are key to the function of riboswitches. These regulatory mRNA regions specifically bind to cellular metabolites using evolutionarily conserved sensing domains and modulate gene expression via adjacent downstream expression platforms, which carry gene expression signals. The regulation is achieved through the ligand-dependent formation of two alternative and mutually exclusive conformations involving the same RNA region. While X-ray crystallography cannot visualize dynamics of such dramatic conformational rearrangements, this method is pivotal to understand RNA-ligand interaction that stabilize the sensing domain and drive folding of the expression platform. X-ray crystallography can reveal local changes in RNA necessary for discriminating cognate and noncognate ligands. This chapter describes preparation of thiamine pyrophosphate riboswitch RNAs and its crystallization with different ligands, resulting in structures with local conformational changes in RNA. These structures can help to derive information on the dynamics of the RNA essential for specific binding to small molecules, with potential for using this information for developing designer riboswitch-ligand systems.

Small-Molecule 3D Ligand for RNA Recognition: Tuning Selectivity through Scaffold Hopping

Targeting RNAs with small molecules is considered the next frontier for drug discovery. In this context, the development of compounds capable of binding RNA structural motifs of low complexity with high affinity and selectivity would greatly expand the number of targets of potential therapeutic value. In this study, we demonstrate that tuning the three-dimensional shape of promiscuous nucleic acid binders is a valuable strategy for the design of new selective RNA ligands. Indeed, starting from a known cyanine, the simple replacement of a phenyl ring with a [2.2]paracyclophane moiety led to a new compound able to discriminate between nucleic acids showing different structural characteristics with a marked affinity and selectivity for an octahairpin loop RNA sequence. This shape modification also affected the in cellulo behavior of the cyanine. These results suggest that scaffold hopping is a valuable strategy to improve the selectivity of RNA/small-molecule interactions and highlight the need to explore a new chemical space for the design of selective RNA ligands.

A toolkit for the identification of NEAT1_2/paraspeckle modulators

Paraspeckles are ribonucleoprotein granules assembled by NEAT1_2 lncRNA, an isoform of Nuclear Paraspeckle Assembly Transcript 1 (NEAT1). Dysregulation of NEAT1_2/paraspeckles has been linked to multiple human diseases making them an attractive drug target. However currently NEAT1_2/paraspeckle-focused translational research and drug discovery are hindered by a limited toolkit. To fill this gap, we developed and validated a set of tools for the identification of NEAT1_2 binders and modulators comprised of biochemical and cell-based assays. The NEAT1_2 triple helix stability element was utilized as the target in the biochemical assays, and the cellular assay ('ParaQuant') was based on high-content imaging of NEAT1_2 in fixed cells. As a proof of principle, these assays were used to screen a 1,200-compound FDA-approved drug library and a 170-compound kinase inhibitor library and to confirm the screening hits. The assays are simple to establish, use only commercially-available reagents and are scalable for higher throughput. In particular, ParaQuant is a cost-efficient assay suitable for any cells growing in adherent culture and amenable to multiplexing. Using ParaQuant, we identified dual PI3K/mTOR inhibitors as potent negative modulators of paraspeckles. The tools we describe herein should boost paraspeckle studies and help guide the search, validation and optimization of NEAT1_2/paraspeckle-targeted small molecules.

Targeting non-coding RNA family members with artificial endonuclease XNAzymes

Non-coding RNAs (ncRNAs) offer a wealth of therapeutic targets for a range of diseases. However, secondary structures and high similarity within sequence families make specific knockdown challenging. Here, we engineer a series of artificial oligonucleotide enzymes (XNAzymes) composed of 2'-deoxy-2'-fluoro-β-D-arabino nucleic acid (FANA) that specifically or preferentially cleave individual ncRNA family members under quasi-physiological conditions, including members of the classic microRNA cluster miR-17~92 (oncomiR-1) and the Y RNA hY5. We demonstrate self-assembly of three anti-miR XNAzymes into a biostable catalytic XNA nanostructure, which targets the cancer-associated microRNAs miR-17, miR-20a and miR-21. Our results provide a starting point for the development of XNAzymes as a platform technology for precision knockdown of specific non-coding RNAs, with the potential to reduce off-target effects compared with other nucleic acid technologies.

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.

RNA-Ligand Interactions Quantified by Surface Plasmon Resonance with Reference Subtraction

Structured RNAs bind ligands and are attractive targets for small-molecule drugs. A wide variety of analytical methods have been used to characterize RNA-ligand interactions, but our experience is that most have significant limitations in terms of material requirements and applicability to complex RNAs. Surface plasmon resonance (SPR) potentially overcomes these limitations, but we find that the standard experimental framework measures notable nonspecific electrostatic-mediated interactions, frustrating analysis of weak RNA binders. SPR measurements are typically quantified relative to a non-target reference channel. Here, we show that referencing to a channel containing a non-binding control RNA enables subtraction of nonspecific binding contributions, allowing measurements of accurate and specific binding affinities. We validated this approach for small-molecule binders of two riboswitch RNAs with affinities ranging from nanomolar to millimolar, including low-molecular-mass fragment ligands. SPR implemented with reference subtraction reliably discriminates specific from nonspecific binding, uses RNA and ligand material efficiently, and enables rapid exploration of the ligand-binding landscape for RNA targets.

Interrogating RNA-Small Molecule Interactions with Structure Probing and Artificial Intelligence-Augmented Molecular Simulations

While there is increasing interest in the study of RNA as a therapeutic target, efforts to understand RNA-ligand recognition at the molecular level lag far behind our understanding of protein-ligand recognition. This problem is complicated due to the more than 10 orders of magnitude in time scales involved in RNA dynamics and ligand binding events, making it not straightforward to design experiments or simulations. Here, we make use of artificial intelligence (AI)-augmented molecular dynamics simulations to directly observe ligand dissociation for cognate and synthetic ligands from a riboswitch system. The site-specific flexibility profiles from our simulations are compared with in vitro measurements of flexibility using selective 2' hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP). Our simulations reproduce known relative binding affinity profiles for the cognate and synthetic ligands, and pinpoint how both ligands make use of different aspects of riboswitch flexibility. On the basis of our dissociation trajectories, we also make and validate predictions of pairs of mutations for both the ligand systems that would show differing binding affinities. These mutations are distal to the binding site and could not have been predicted solely on the basis of structure. The methodology demonstrated here shows how molecular dynamics simulations with all-atom force-fields have now come of age in making predictions that complement existing experimental techniques and illuminate aspects of systems otherwise not trivial to understand.

R-BIND 2.0: An Updated Database of Bioactive RNA-Targeting Small Molecules and Associated RNA Secondary Structures

Discoveries of RNA roles in cellular physiology and pathology are increasing the need for new tools that modulate the structure and function of these biomolecules, and small molecules are proving useful. In 2017, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND) and discovered distinguishing physicochemical properties of RNA-targeting ligands, leading us to propose the existence of an "RNA-privileged" chemical space. Biennial updates of the database and the establishment of a website platform (rbind.chem.duke.edu) have provided new insights and tools to design small molecules based on the analyzed physicochemical and spatial properties. In this report and R-BIND 2.0 update, we refined the curation approach and ligand classification system as well as conducted analyses of RNA structure elements for the first time to identify new targeting strategies. Specifically, we curated and analyzed RNA target structural motifs to determine the properties of small molecules that may confer selectivity for distinct RNA secondary and tertiary structures. Additionally, we collected sequences of target structures and incorporated an RNA structure search algorithm into the website that outputs small molecules targeting similar motifs without a priori secondary structure knowledge. Cheminformatic analyses revealed that, despite the 50% increase in small molecule library size, the distinguishing properties of R-BIND ligands remained significantly different from that of proteins and are therefore still relevant to RNA-targeted probe discovery. Combined, we expect these novel insights and website features to enable the rational design of RNA-targeted ligands and to serve as a resource and inspiration for a variety of scientists interested in RNA targeting.