Inforna 2.0: A Platform for the Sequence-Based Design of Small Molecules Targeting Structured RNAs

Structures of small molecules bound to RNA repeat expansions that cause Huntington's disease-like 2 and myotonic dystrophy type 1

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

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

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

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

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.

Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system

Targeting RNA including viral RNAs with small molecules is an emerging field. The hepatitis C virus internal ribosome entry site (HCV IRES) is a potential target for translation inhibitor development to raise drug resistance mutation preparedness. Using RNA-focused and unbiased molecule libraries, a structure-based virtual screening (VS) by molecular docking and pharmacophore analysis was performed against the HCV IRES subdomain IIa. VS hits were validated by a microscale thermophoresis (MST) binding assay and a Förster resonance energy transfer (FRET) assay elucidating ligand-induced conformational changes. Ten hit molecules were identified with potencies in the high to medium micromolar range proving the suitability of structure-based virtual screenings against RNA-targets. Hit compounds from a 2-guanidino-quinazoline series, like the strongest binder, compound 8b with an EC50 of 61 μM, show low molecular weight, moderate lipophilicity and reduced basicity compared to previously reported IRES ligands. Therefore, it can be considered as a potential starting point for further optimization by chemical derivatization.

High-resolution RNA tertiary structures in Zika virus stem-loop A for the development of inhibitory small molecules

Flaviviruses such as Zika (ZIKV) and dengue virus (DENV) are positive-sense RNA viruses belonging to Flaviviridae The flavivirus genome contains a 5' end stem-loop promoter sequence known as stem-loop A (SLA) that is recognized by the flavivirus polymerase NS5 during viral RNA synthesis and 5' guanosine cap methylation. The crystal structures of ZIKV and DENV SLAs show a well-defined fold, consisting of a bottom stem, side loop, and top stem-loop, providing unique interaction sites for small molecule inhibitors to disrupt the promoter function. To facilitate the identification of small molecule binding sites in flavivirus SLA, we determined high-resolution structures of the bottom and top stems of ZIKV SLA, which contain a single U- or G-bulge, respectively. Both bulge nucleotides exhibit multiple orientations, from folded back on the adjacent nucleotide to flipped out of the helix, and are stabilized by stacking or base triple interactions. These structures suggest that even a single unpaired nucleotide can provide flexibility to RNA structures, and its conformation is mainly determined by the stabilizing chemical environment. To facilitate discovery of small molecule inhibitors that interfere with the functions of ZIKV SLA, we screened and identified compounds that bind to the bottom and top stems of ZIKV SLA.

Heterobifunctional small molecules to modulate RNA function

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

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.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

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.

Targeting MicroRNAs with Small Molecules

MicroRNAs (miRs) have been implicated in numerous diseases, presenting an attractive target for the development of novel therapeutics. The various regulatory roles of miRs in cellular processes underscore the need for precise strategies. Recent advances in RNA research offer hope by enabling the identification of small molecules capable of selectively targeting specific disease-associated miRs. This understanding paves the way for developing small molecules that can modulate the activity of disease-associated miRs. Herein, we discuss the progress made in the field of drug discovery processes, transforming the landscape of miR-targeted therapeutics by small molecules. By leveraging various approaches, researchers can systematically identify compounds to modulate miR function, providing a more potent intervention either by inhibiting or degrading miRs. The implementation of these multidisciplinary approaches bears the potential to revolutionize treatments for diverse diseases, signifying a significant stride towards the targeting of miRs by precision medicine.

Targeting and engineering long non-coding RNAs for cancer therapy

RNA therapeutics (RNATx) aim to treat diseases, including cancer, by targeting or employing RNA molecules for therapeutic purposes. Amongst the most promising targets are long non-coding RNAs (lncRNAs), which regulate oncogenic molecular networks in a cell type-restricted manner. lncRNAs are distinct from protein-coding genes in important ways that increase their therapeutic potential yet also present hurdles to conventional clinical development. Advances in genome editing, oligonucleotide chemistry, multi-omics and RNA engineering are paving the way for efficient and cost-effective lncRNA-focused drug discovery pipelines. In this Review, we present the emerging field of lncRNA therapeutics for oncology, with emphasis on the unique strengths and challenges of lncRNAs within the broader RNATx framework. We outline the necessary steps for lncRNA therapeutics to deliver effective, durable, tolerable and personalized treatments for cancer.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

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

Advances in Research Related to MicroRNA for Diabetic Retinopathy

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and is one of the primary causes of blindness in the working-age population in Europe and the United States. At present, no cure is available for DR, but early detection and timely intervention can prevent the rapid progression of the disease. Several treatments for DR are known, primarily ophthalmic treatment based on glycemia, blood pressure, and lipid control, which includes laser photocoagulation, glucocorticoids, vitrectomy, and antivascular endothelial growth factor (anti-VEGF) medications. Despite the clinical efficacy of the aforementioned therapies, none of them can entirely shorten the clinical course of DR or reverse retinopathy. MicroRNAs (miRNAs) are vital regulators of gene expression and participate in cell growth, differentiation, development, and apoptosis. MicroRNAs have been shown to play a significant role in DR, particularly in the molecular mechanisms of inflammation, oxidative stress, and neurodegeneration. The aim of this review is to systematically summarize the signaling pathways and molecular mechanisms of miRNAs involved in the occurrence and development of DR, mainly from the pathogenesis of oxidative stress, inflammation, and neovascularization. Meanwhile, this article also discusses the research progress and application of miRNA-specific therapies for DR.

Small molecule approaches to targeting RNA

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

Decreasing the intrinsically disordered protein α-synuclein levels by targeting its structured mRNA with a ribonuclease-targeting chimera

α-Synuclein is an important drug target for the treatment of Parkinson's disease (PD), but it is an intrinsically disordered protein lacking typical small-molecule binding pockets. In contrast, the encoding SNCA mRNA has regions of ordered structure in its 5' untranslated region (UTR). Here, we present an integrated approach to identify small molecules that bind this structured region and inhibit α-synuclein translation. A drug-like, RNA-focused compound collection was studied for binding to the 5' UTR of SNCA mRNA, affording Synucleozid-2.0, a drug-like small molecule that decreases α-synuclein levels by inhibiting ribosomes from assembling onto SNCA mRNA. This RNA-binding small molecule was converted into a ribonuclease-targeting chimera (RiboTAC) to degrade cellular SNCA mRNA. RNA-seq and proteomics studies demonstrated that the RiboTAC (Syn-RiboTAC) selectively degraded SNCA mRNA to reduce its protein levels, affording a fivefold enhancement of cytoprotective effects as compared to Synucleozid-2.0. As observed in many diseases, transcriptome-wide changes in RNA expression are observed in PD. Syn-RiboTAC also rescued the expression of ~50% of genes that were abnormally expressed in dopaminergic neurons differentiated from PD patient-derived iPSCs. These studies demonstrate that the druggability of the proteome can be expanded greatly by targeting the encoding mRNAs with both small molecule binders and RiboTAC degraders.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

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

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

Broad-spectrum metastasis suppressing compounds and therapeutic uses thereof in human tumors

Previously, we have identified a novel human metastasis-inducing lncRNA (named SKAI1BC), that suppresses the KAI1/CD82 metastasis-suppressing gene and is upregulated in triple negative breast cancer and melanoma derived cell lines. Modeling of the SKAI1BC lncRNA secondary structure and its potential interaction with Inforna compounds, led us to identify several compounds that might bind the SKAI1BC lncRNA. We found that these compounds inhibit metastasis invasion and cell migration in culture, in all eight types of solid human cancers tested: several of which are the most lethal and/or frequent human malignancies. Moreover, in most cases, the mechanism of action of several of our compounds involves enhancement of KAI1/CD82 RNA level depending on the specific compound and the human tumor type. With the epigenetic inactivation of KAI1/CD82 in at least ten additional solid human cancers, this implies a very good chance to broaden the spectrum of human cancers affected by our compounds. This is the first time that modeling of a large lncRNA (> 700 bp) secondary structure followed by its potential interaction with Inforna like compounds database has led to the identification of potential biologically active small molecule drugs.

Altering the Cleaving Effector in Chimeric Molecules that Target RNA Enhances Cellular Selectivity

Small molecules that target RNA and effect their cleavage are useful chemical probes and potential lead medicines. In this study, we investigate factors affecting degradation of two cancer-associated RNA targets, the mRNA that encodes the transcription factor JUN (c-Jun) and the hairpin precursor to microRNA-372 (pre-miR-372). The two RNA targets harbor the same small-molecule binding site juxtaposed with different neighboring structures. Specifically, pre-miR-372 has AU pairs and contiguous purines on one strand near the small-molecule binding site, making it an ideal substrate for oxidative cleavage via the direct degrader bleomycin A5. In contrast, while JUN mRNA has a similar number of AU pairs near the small-molecule binding site, it lacks contiguous purine nucleotides. Instead, it contains unpaired pyrimidine nucleotides, which are preferred substrates for RNase L, a ribonuclease that can be recruited to RNA with heterobifunctional ribonuclease targeting chimeras (RiboTACs). We hypothesized that structural features surrounding the binding site could be leveraged to program selective small-molecule degradation by alteration of the cleaving module. Indeed, the bleomycin degrader cleaves pre-miR-372 in gastric cancer cells but not JUN mRNA. Conversely, the RiboTAC cleaves JUN mRNA but not pre-miR-372. Thus, the selection of the appropriate cleaving effector moiety for an RNA-binding small molecule can be leveraged to cleave an RNA selectively in a predictable manner, which could have broad implications.

Optimization of a Protein-Targeted Medicine into an RNA-Specific Small Molecule

Protein-targeted small molecule medicines often bind RNAs and affect RNA-mediated pathways in cells. Historically, small molecule engagement and modulation of RNA have not been considered in medicine development; however, RNA should be considered both a potential on- and off-target. Kinase inhibitors have emecrged as common RNA binders with dovitinib, a classic receptor tyrosine kinase (RTK) inhibitor, inhibiting RTKs and the biogenesis of oncogenic microRNA-21 through direct engagement. In this study, we use knowledge of the molecular recognition of both protein and RNA targets by dovitinib to design molecules that specifically inhibit the RNA target but lack activity against canonical protein targets in cells. As it is now becoming apparent that RNA can be both an on- and off-target for small molecule medicines, this study lays a foundation to use design principles to maximize desired compound activity while minimizing off-target effects.

Agents for the Treatment of Gout: Current Advances and Future Perspectives

Gout is characterized by hyperuricemia and the deposition of monosodium urate (MSU) crystals around joints. Despite the availability of several drugs on the market, its treatment remains challenging owing to the notable side effects, such as hepatorenal toxicity and cardiovascular complications, that are associated with most existing agents. This perspective aims to summarize the current research progress in the development of antigout agents, particularly focusing on xanthine oxidase (XO) and urate anion transporter 1 (URAT1) inhibitors from a medicinal chemistry viewpoint and their preliminary structure-activity relationships (SARs). This perspective provides valuable insights and theoretical guidance to medicinal chemists for the discovery of antigout agents with novel chemical structures, better efficiency, and lower toxicity.

Chemical-guided SHAPE sequencing (cgSHAPE-seq) informs the binding site of RNA-degrading chimeras targeting SARS-CoV-2 5' untranslated region

One of the hallmarks of RNA viruses is highly structured untranslated regions (UTRs) in their genomes. These conserved RNA structures are often essential for viral replication, transcription, or translation. In this report, we discovered and optimized a new type of coumarin derivatives, such as C30 and C34, which bind to a four-way RNA helix called SL5 in the 5' UTR of the SARS-CoV-2 RNA genome. To locate the binding site, we developed a novel sequencing-based method namely cgSHAPE-seq, in which the acylating chemical probe was directed to crosslink with the 2'-OH groups of ribose at the ligand binding site. This crosslinked RNA could then create read-through mutations during reverse transcription (i.e., primer extension) at single-nucleotide resolution to uncover the acylation locations. cgSHAPE-seq unambiguously determined that a bulged G in SL5 was the primary binding site of C30 in the SARS-CoV-2 5' UTR, which was validated through mutagenesis and in vitro binding experiments. C30 was further used as a warhead in RNA-degrading chimeras to reduce viral RNA expression levels. We demonstrated that replacing the acylating moiety in the cgSHAPE probe with ribonuclease L recruiter (RLR) moieties yielded RNA degraders active in the in vitro RNase L degradation assay and SARS-CoV-2 5' UTR expressing cells. We further explored another RLR conjugation site on the E ring of C30/C34 and discovered improved RNA degradation activities in vitro and in cells. The optimized RNA-degrading chimera C64 inhibited live virus replication in lung epithelial carcinoma cells.

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.

Synthesis of Bleomycin-Inspired RNA Ligands Targeting the Biogenesis of Oncogenic miRNAs

Noncoding RNAs (ncRNAs) play pivotal roles in the regulation of gene expression and represent a promising target for the development of new therapeutic approaches. Among these ncRNAs, microRNAs (miRNAs or miRs) are involved in the regulation of gene expression, and their dysregulation has been linked to several diseases such as cancers. Indeed, oncogenic miRNAs are overexpressed in cancer cells, thus promoting tumorigenesis and maintenance of cancer stem cells that are resistant to chemotherapy and often responsible for therapeutic failure. Here, we describe the design and synthesis of new small-molecule RNA binders able to inhibit the biogenesis of oncogenic miRNAs and target efficiently cancer stem cells. Through the biochemical study of their interaction with the target and thanks to intracellular assays, we describe the structure-activity relationships for this new series of RNA ligands, and we identify compounds bearing a very promising antiproliferative activity against cancer stem cells.

Long Noncoding RNAs in Plant-Pathogen Interactions

Long noncoding RNAs (lncRNAs) are commonly defined as transcripts that lack protein-coding capacity and are longer than 200 nucleotides. Since the emergence of next-generation sequencing technologies in this century, thousands of lncRNAs have been identified from nearly all living organisms. Notably, various pathogens also express their own lncRNAs in host cells during infection. In plants, many lncRNAs exhibit dynamic expression patterns in response to environmental stimuli, including pathogen attacks. In contrast to well-established methods in identifying such lncRNAs, the current understanding of lncRNAs' functional mechanisms is in its infancy. Some lncRNAs serve as precursors for generating small RNAs or serve as target mimics to sequester functional small RNAs, which have been extensively reviewed in the literature. This review focuses on the emerging evidence supporting that certain lncRNAs function as negative or positive regulators of plant immunity. A common theme is that those regulations rely on specific interactions between lncRNAs and key regulatory proteins. Viroids as single-stranded circular noncoding RNAs provide a handle to investigate how RNA local motifs render interaction specificity between lncRNAs and regulatory proteins. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

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.

G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA

The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.

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

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

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Long non-coding RNA-targeting therapeutics: discovery and development update

INTRODUCTION

lncRNAs are major players in regulatory networks orchestrating multiple cellular functions, such as 3D chromosomal interactions, epigenetic modifications, gene expression and others. Due to progress in the development of nucleic acid-based therapeutics, lncRNAs potentially represent easily accessible therapeutic targets.

AREAS COVERED

Currently, significant efforts are directed at studies that can tap the enormous therapeutic potential of lncRNAs. This review describes recent developments in this field, particularly focusing on clinical applications.

EXPERT OPINION

Extensive druggable target range of lncRNA combined with high specificity and accelerated development process of nucleic acid-based therapeutics open new prospects for treatment in areas of extreme unmet medical need, such as genetic diseases, aggressive cancers, protein deficiencies, and subsets of common diseases caused by known mutations. Although currently wide acceptance of lncRNA-targeting nucleic acid-based therapeutics is impeded by the need for parenteral or direct-to-CNS administration, development of less invasive techniques and orally available/BBB-penetrant nucleic acid-based therapeutics is showing early successes. Recently, mRNA-based COVID-19 vaccines have demonstrated clinical safety of all aspects of nucleic acid-based therapeutic technology, including multiple chemical modifications of nucleic acids and nanoparticle delivery. These trends position lncRNA-targeting drugs as significant players in the future of drug development, especially in the area of personalized medicine.

Amplifying gene expression with RNA-targeted therapeutics

Many diseases are caused by insufficient expression of mutated genes and would benefit from increased expression of the corresponding protein. However, in drug development, it has been historically easier to develop drugs with inhibitory or antagonistic effects. Protein replacement and gene therapy can achieve the goal of increased protein expression but have limitations. Recent discoveries of the extensive regulatory networks formed by non-coding RNAs offer alternative targets and strategies to amplify the production of a specific protein. In addition to RNA-targeting small molecules, new nucleic acid-based therapeutic modalities that allow highly specific modulation of RNA-based regulatory networks are being developed. Such approaches can directly target the stability of mRNAs or modulate non-coding RNA-mediated regulation of transcription and translation. This Review highlights emerging RNA-targeted therapeutics for gene activation, focusing on opportunities and challenges for translation to the clinic.

Programming inactive RNA-binding small molecules into bioactive degraders

Target occupancy is often insufficient to elicit biological activity, particularly for RNA, compounded by the longstanding challenges surrounding the molecular recognition of RNA structures by small molecules. Here we studied molecular recognition patterns between a natural-product-inspired small-molecule collection and three-dimensionally folded RNA structures. Mapping these interaction landscapes across the human transcriptome defined structure-activity relationships. Although RNA-binding compounds that bind to functional sites were expected to elicit a biological response, most identified interactions were predicted to be biologically inert as they bind elsewhere. We reasoned that, for such cases, an alternative strategy to modulate RNA biology is to cleave the target through a ribonuclease-targeting chimera, where an RNA-binding molecule is appended to a heterocycle that binds to and locally activates RNase L1. Overlay of the substrate specificity for RNase L with the binding landscape of small molecules revealed many favourable candidate binders that might be bioactive when converted into degraders. We provide a proof of concept, designing selective degraders for the precursor to the disease-associated microRNA-155 (pre-miR-155), JUN mRNA and MYC mRNA. Thus, small-molecule RNA-targeted degradation can be leveraged to convert strong, yet inactive, binding interactions into potent and specific modulators of RNA function.

Fragment-Based Approaches to Identify RNA Binders

Although fragment-based drug discovery (FBDD) has been successfully implemented and well-explored for protein targets, its feasibility for RNA targets is emerging. Despite the challenges associated with the selective targeting of RNA, efforts to integrate known methods of RNA binder discovery with fragment-based approaches have been fruitful, as a few bioactive ligands have been identified. Here, we review various fragment-based approaches implemented for RNA targets and provide insights into experimental design and outcomes to guide future work in the area. Indeed, investigations surrounding the molecular recognition of RNA by fragments address rather important questions such as the limits of molecular weight that confer selective binding and the physicochemical properties favorable for RNA binding and bioactivity.

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

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

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

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

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.

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.

4-Aminoquinolines modulate RNA structure and function: Pharmacophore implications of a conformationally restricted polyamine

RNA structure plays an important role in regulating cellular function and there is a significant emerging interest in targeting RNA for drug discovery. Here we report the identification of 4-aminoquinolines as modulators of RNA structure and function. Aminoquinolines have a broad range of pharmacological activities, but their specific mechanism of action is often not fully understood. Using electrophoretic mobility shift assays and enzymatic probing we identified 4-aminoquinolines that bind the stem-loop II motif (s2m) of SARS-CoV-2 RNA site-specifically and induce dimerization. Using fluorescence-based RNA binding and T-box riboswitch functional assays we identified that hydroxychloroquine binds the T-box riboswitch antiterminator RNA element and inhibits riboswitch function. Based on its structure and riboswitch dose-response activity we identified that the antagonist activity of hydroxychloroquine is consistent with it being a conformationally restricted analog of the polyamine spermidine. Given the known role that polyamines play in RNA function, the identification of an RNA binding ligand with the pharmacophore of a conformationally restricted polyamine has significant implications for further elucidation of RNA structure-function relationships and RNA-targeted drug discovery.

Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies

INTRODUCTION

The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approved risdiplam, the biopharmaceutical industry is gaining confidence in the field. This review, based on the literature obtained from PubMed, aims to disseminate information about the various technologies developed for targeting RNA with small molecules and propose areas for improvement to develop drugs more efficiently, particularly those linked to diseases with unmet medical needs.

AREAS COVERED

The technologies for the identification of RNA targets, screening of chemical libraries against RNA, assessing the bioactivity and target engagement of the hit compounds, structure determination, and hit-to-lead optimization are reviewed. Along with the description of the technologies, their strengths, limitations, and examples of how they can impact drug discovery are provided.

EXPERT OPINION

Many existing technologies employed for protein targets have been repurposed for use in the discovery of RNA-targeted small molecules. In addition, technologies tailored for RNA targets have been developed. Nevertheless, more improvements are necessary, such as artificial intelligence to dissect important RNA structures and RNA-small-molecule interactions and more powerful chemical probing and structure prediction techniques.

Strategies for targeting RNA with small molecule drugs

INTRODUCTION

Historically, therapeutic treatment of disease has been restricted to targeting proteins. Of the approximately 20,000 translated human proteins, approximately 1600 are associated with diseases. Strikingly, less than 15% of disease-associated proteins are predicted or known to be 'druggable.' While the concept and narrative of protein druggability continue to evolve with the development of novel technological and pharmacological advances, most of the human proteome remains undrugged. Recent genomic studies indicate that less than 2% of the human genome encodes for proteins, and while as much as 75% of the genome is transcribed, RNA has largely been ignored as a druggable target for therapeutic interventions.

AREAS COVERED

This review delineates the theory and techniques involved in the development of small molecule inhibitors of RNAs from brute force, high-throughput screening technologies to de novo molecular design using computational machine and deep learning. We will also highlight the potential pitfalls and limitations of targeting RNA with small molecules.

EXPERT OPINION

Although significant advances have recently been made in developing systems to identify small molecule inhibitors of RNAs, many challenges remain. Focusing on RNA structure and ligand binding sites may help bring drugging RNA in line with traditional protein drug targeting.

Targeting RNA with small molecules-A safety perspective

RNA is a major player in cellular function, and consequently can drive a number of disease pathologies. Over the past several years, small molecule-RNA targeting (smRNA targeting) has developed into a promising drug discovery approach. Numerous techniques, tools, and assays have been developed to support this field, and significant investments have been made by pharmaceutical and biotechnology companies. To date, the focus has been on identifying disease validated primary targets for smRNA drug development, yet RNA as a secondary (off) target for all small molecule drug programs largely has been unexplored. In this perspective, we discuss structure, target, and mechanism-driven safety aspects of smRNAs and highlight how these parameters can be evaluated in drug discovery programs to produce potentially safer drugs.

Gentamycin Rationally Repositioned to Inhibit miR-34a Ameliorates Oxidative Injury to PC12 Cells

Ischemic stroke accompanies oxidative stress and cell death in the cerebral tissue. The microRNA miR-34a plays a pivotal role in this molecular pathology. This study presents the rational repositioning of aminoglycosidic antibiotics as miR-34a antagonists in order to assess their efficiency in protecting the PC12 stroke model cells from oxidative stress occurring under cerebral ischemic conditions. A library of 29 amino-sugar compounds were screened against anticipated structural models of miR-34a through molecular docking. MiR-ligand interactions were mechanistically studied by molecular dynamics simulations and free-energy calculations. Cultured PC12 cells were treated by H₂O₂ alone or in combination with gentamycin and neomycin as selected drugs. Cell viability and apoptosis were detected by 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) and annexin V-FITC/propidium iodate (PI) double staining assays, respectively. The expression levels of key factors involved in cell proliferation, oxidative stress, and apoptosis in treated PC12 cells were measured through a quantitative real-time polymerase chain reaction and flow cytometric annexin V-FITC/PI double staining assays. A stable and energetically favorable binding was observed for miR-34a with gentamycin and neomycin. Gentamycin pretreatments followed by H₂O₂ oxidative injury led to increased cell viability and protected PC12 cells against H₂O₂-induced apoptotic events. This study will help in further understanding how the suppression of miR-34a in neural tissue affects the cell viability upon stroke.

Noncoding RNA therapeutics for substance use disorder

Although noncoding RNAs (ncRNAs) have been shown to regulate maladaptive neuroadaptations that drive compulsive drug use, ncRNA-targeting therapeutics for substance use disorder (SUD) have yet to be clinically tested. Recent advances in RNA-based drugs have improved many therapeutic issues related to immune response, specificity, and delivery, leading to multiple successful clinical trials for other diseases. As the need for safe and effective treatments for SUD continues to grow, novel nucleic acid-based therapeutics represent an appealing approach to target ncRNA mechanisms in SUD. Here, we review ncRNA processes implicated in SUD, discuss recent therapeutic approaches for targeting ncRNAs, and highlight potential opportunities and challenges of ncRNA-targeting therapeutics for SUD.

Study of an RNA-Focused DNA-Encoded Library Informs Design of a Degrader of a r(CUG) Repeat Expansion

A solid-phase DNA-encoded library (DEL) was studied for binding the RNA repeat expansion r(CUG)^exp, the causative agent of the most common form of adult-onset muscular dystrophy, myotonic dystrophy type 1 (DM1). A variety of uncharged and novel RNA binders were identified to selectively bind r(CUG)^exp by using a two-color flow cytometry screen. The cellular activity of one binder was augmented by attaching it with a module that directly cleaves r(CUG)^exp. In DM1 patient-derived muscle cells, the compound specifically bound r(CUG)^exp and allele-specifically eliminated r(CUG)^exp, improving disease-associated defects. The approaches herein can be used to identify and optimize ligands and bind RNA that can be further augmented for functionality including degradation.

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.

Trading places: Peptide and small molecule alternatives to oligonucleotide-based modulation of microRNA expression

It is well established that microRNA (miRNA) dysregulation is involved in the development and progression of various diseases, especially cancer. Emerging evidence suggests that small molecule and peptide agents can interfere with miRNA disease pathways. Despite this, very little is known about structural features that drive drug-miRNA interactions and subsequent inhibition. In this review, we highlight the advances made in the development of small molecule and peptide inhibitors of miRNA processing. Specifically, we attempt to draw attention to peptide features that may be critical for interaction with the miRNA secondary structure to regulate miRNA expression. We hope that this review will help to establish peptides as exciting miRNA expression modulators and will contribute towards the development of the first miRNA-targeting peptide therapy.