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

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.

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.

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.

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

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

Autophagy regulation by RNA alternative splicing and implications in human diseases

Autophagy and RNA alternative splicing are two evolutionarily conserved processes involved in overlapping physiological and pathological processes. However, the extent of functional connection is not well defined. Here, we consider the role for alternative splicing and generation of autophagy-related gene isoforms in the regulation of autophagy in recent work. The impact of changes to the RNA alternative splicing machinery and production of alternative spliced isoforms on autophagy are reviewed with particular focus on disease relevance. The use of drugs targeting both alternative splicing and autophagy as well as the selective regulation of single autophagy-related protein isoforms, are considered as therapeutic strategies.

Both alternative splicing and autophagy are core cell biological processes, but where they intersect has received little attention. Here, the authors reflect on recent connections identified between these pathways and consider their impact on human disease.

SHAPE-enabled fragment-based ligand discovery for RNA

The transcriptome represents an attractive but underused set of targets for small-molecule ligands. Here, we devise a technology that leverages fragment-based screening and SHAPE-MaP RNA structure probing to discover small-molecule fragments that bind an RNA structure of interest. We identified fragments and cooperatively binding fragment pairs that bind to the thiamine pyrophosphate (TPP) riboswitch with millimolar to micromolar affinities. We then used structure-activity relationship information to efficiently design a linked-fragment ligand, with no resemblance to the native ligand, with high ligand efficiency and druglikeness, that binds to the TPP thiM riboswitch with high nanomolar affinity and that modulates RNA conformation during cotranscriptional folding. Principles from this work are broadly applicable, leveraging cooperativity and multisite binding, for developing high-quality ligands for diverse RNA targets.

RNA-Binding Macrocyclic Peptides

Being able to effectively target RNA with potent ligands will open up a large number of potential therapeutic options. The knowledge on how to achieve this is ever expanding but an important question that remains open is what chemical matter is suitable to achieve this goal. The high flexibility of an RNA as well as its more limited chemical diversity and featureless binding sites can be difficult to target selectively but can be addressed by well-designed cyclic peptides. In this review we will provide an overview of reported cyclic peptide ligands for therapeutically relevant RNA targets and discuss the methods used to discover them. We will also provide critical insights into the properties required for potent and selective interaction and suggestions on how to assess these parameters. The use of cyclic peptides to target RNA is still in its infancy but the lessons learned from past examples can be adopted for the development of novel potent and selective ligands.

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.

Zooming in on Long Non-Coding RNAs in Ewing Sarcoma Pathogenesis

Ewing sarcoma (ES) is a rare aggressive cancer of bone and soft tissue that is mainly characterized by a reciprocal chromosomal translocation. As a result, about 90% of cases express the EWS-FLI1 fusion protein that has been shown to function as an aberrant transcription factor driving sarcomagenesis. ES is the second most common malignant bone tumor in children and young adults. Current treatment modalities include dose-intensified chemo- and radiotherapy, as well as surgery. Despite these strategies, patients who present with metastasis or relapse still have dismal prognosis, warranting a better understanding of treatment resistant-disease biology in order to generate better prognostic and therapeutic tools. Since the genomes of ES tumors are relatively quiet and stable, exploring the contributions of epigenetic mechanisms in the initiation and progression of the disease becomes inevitable. The search for novel biomarkers and potential therapeutic targets of cancer metastasis and chemotherapeutic drug resistance is increasingly focusing on long non-coding RNAs (lncRNAs). Recent advances in genome analysis by high throughput sequencing have immensely expanded and advanced our knowledge of lncRNAs. They are non-protein coding RNA species with multiple biological functions that have been shown to be dysregulated in many diseases and are emerging as crucial players in cancer development. Understanding the various roles of lncRNAs in tumorigenesis and metastasis would determine eclectic avenues to establish therapeutic and diagnostic targets. In ES, some lncRNAs have been implicated in cell proliferation, migration and invasion, features that make them suitable as relevant biomarkers and therapeutic targets. In this review, we comprehensively discuss known lncRNAs implicated in ES that could serve as potential biomarkers and therapeutic targets of the disease. Though some current reviews have discussed non-coding RNAs in ES, to our knowledge, this is the first review focusing exclusively on ES-associated lncRNAs.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

A structure-specific small molecule inhibits a miRNA-200 family member precursor and reverses a type 2 diabetes phenotype

MicroRNA families are ubiquitous in the human transcriptome, yet targeting of individual members is challenging because of sequence homology. Many secondary structures of the precursors to these miRNAs (pri- and pre-miRNAs), however, are quite different. Here, we demonstrate both in vitro and in cellulis that design of structure-specific small molecules can inhibit a particular miRNA family member to modulate a disease pathway. The miR-200 family consists of five miRNAs, miR-200a, -200b, -200c, -141, and -429, and is associated with type 2 diabetes (T2D). We designed a small molecule that potently and selectively targets pre-miR-200c's structure and reverses a pro-apoptotic effect in a pancreatic β cell model. In contrast, an oligonucleotide targeting the RNA's sequence inhibited all family members. Global proteomics and RNA sequencing analyses further demonstrate selectivity for miR-200c. Collectively, these studies establish that miR-200c plays an important role in T2D, and small molecules targeting RNA structure can be an important complement to oligonucleotides.

DNA-encoded library versus RNA-encoded library selection enables design of an oncogenic noncoding RNA inhibitor

Drug discovery generally investigates one target at a time, in sharp contrast to living organisms, which mold ligands and targets by evolution of highly complex molecular interaction networks. We recapitulate this modality of discovery by encoding drug structures in DNA, allowing the entire DNA-encoded library to interact with thousands of RNA fold targets, and then decoding both drug and target by sequencing. This information serves as a filter to identify human RNAs aberrantly produced in cancer that are also binding partners of the discovered ligand, leading to a precision medicine candidate that selectively ablates an oncogenic noncoding RNA, reversing a disease-associated phenotype in cells.

Nature evolves molecular interaction networks through persistent perturbation and selection, in stark contrast to drug discovery, which evaluates candidates one at a time by screening. Here, nature’s highly parallel ligand-target search paradigm is recapitulated in a screen of a DNA-encoded library (DEL; 73,728 ligands) against a library of RNA structures (4,096 targets). In total, the screen evaluated ∼300 million interactions and identified numerous bona fide ligand–RNA three-dimensional fold target pairs. One of the discovered ligands bound a 5′GAG/3′CCC internal loop that is present in primary microRNA-27a (pri-miR-27a), the oncogenic precursor of microRNA-27a. The DEL-derived pri-miR-27a ligand was cell active, potently and selectively inhibiting pri-miR-27a processing to reprogram gene expression and halt an otherwise invasive phenotype in triple-negative breast cancer cells. By exploiting evolutionary principles at the earliest stages of drug discovery, it is possible to identify high-affinity and selective target–ligand interactions and predict engagements in cells that short circuit disease pathways in preclinical disease models.

Expanding the known structure space for RNA binding: a test of 2,5-diketopiperazine

As the importance of RNA as a therapeutic target has become increasingly recognized, the need for new chemotypes able to bind RNA has grown in significance. We hypothesized that diketopiperazines (DKPs), common substructures in natural products and protein-targeting therapeutic agents, could serve as effective scaffolds for targeting RNA. To confirm this hypothesis, we designed and synthesized two analogs, one incorporating a DKP and one not, of compounds previously demonstrated to bind an RNA critical to the life cycle of HIV-1 with high affinity and specificity. Prior to compound synthesis, calculations employing density functional methods and molecular mechanics conformational searches were used to confirm that the DKP could present functionality in a similar (albeit not identical) orientation to the non DKP-containing compound. We found that both the DKP-containing and parent compound had similar affinities to the target RNA as measured by surface plasmon resonance (SPR). Both compounds were found to have modest but equal anti-HIV activity. These results establish the feasibility of using DKPs to target RNA.