Small molecule recognition of disease-relevant RNA structures

Bioorthogonal Cyclopropenones for Investigating RNA Structure

RNA sequences encode structures that impact protein production and other cellular processes. Misfolded RNAs can also potentiate disease, but a complete picture is lacking. To establish more comprehensive and accurate RNA structure-function relationships, new methods are needed to interrogate RNA in native environments. Existing tools rely primarily on electrophiles that are constitutively "on" or triggered by UV light, often resulting in high background. Here we describe an alternative, chemically triggered approach to cross-link RNAs using bioorthogonal cyclopropenones (CpOs). These reagents selectively react with phosphines to provide ketenes─electrophiles that can trap neighboring nucleophiles to forge covalent cross-links. As a proof-of-concept, we conjugated a CpO motif to thiazole orange (TO-1). TO-1-CpO bound selectively to a model RNA aptamer (Mango) with nanomolar affinity, as confirmed by fluorescence turn-on. After phosphine administration, covalent cross-links were formed between the CpO and RNA. Cross-linking was both time and dose dependent. We further applied the chemically triggered tools to model RNAs under biologically relevant conditions. Collectively, this work expands the toolkit of probes for studying RNA and its native conformations.

Unveiling the promise of peptide nucleic acids as functional linkers for an RNA imaging platform

Linkers in chemical biology provide more than just connectivity between molecules; their intrinsic properties can be harnessed to enhance the stability and functionality of chemical probes. In this study, we explored the incorporation of a peptide nucleic acid (PNA)-based linker into RNA-targeting probes to improve their affinity and specificity. By integrating a PNA linker into a small molecule probe of the Riboglow platform, we enabled dual binding events: cobalamin (Cbl)-RNA structure-based recognition and sequence-specific PNA-RNA interaction. We show that incorporating a six-nucleotide PNA sequence complementary to a region of wild type RNA aptamer (env8) results in a 30-fold improvement in binding affinity compared to the probe with a nonfunctional PEG linker. Even greater improvements are observed when the PNA probe was tested against truncated versions of the RNA aptamer, with affinity increasing by up to 280-fold. Additionally, the PNA linker is able to rescue the Cbl-RNA interaction even when the cobalamin binding pocket is compromised. We demonstrate that PNA probes effectively bind RNA both in vitro and in live cells, enhancing visualization of RNA in stress granules and U-bodies at low concentrations. The modular nature of the Riboglow platform allows for flexible modifications of the PNA linker, fluorophore, and RNA tag, while maintaining high specificity and affinity. This work establishes a new approach for enhancing RNA imaging platforms through the use of PNA linkers, highlighting the potential of combining short oligonucleotides with small molecules to improve the affinity and specificity of RNA-targeting probes. Furthermore, this dual-binding approach presents a promising strategy for driving advancements in RNA-targeted drug development.

Comparison of Three Computational Tools for the Prediction of RNA Tertiary Structures

Understanding the structures of noncoding RNAs (ncRNAs) is important for the development of RNA-based therapeutics. There are inherent challenges in employing current experimental techniques to determine the tertiary (3D) structures of RNAs with high complexity and flexibility in folding, which makes computational methods indispensable. In this study, we compared the utilities of three advanced computational tools, namely RNAComposer, Rosetta FARFAR2, and the latest AlphaFold 3, to predict the 3D structures of various forms of RNAs, including the small interfering RNA drug, nedosiran, and the novel bioengineered RNA (BioRNA) molecule showing therapeutic potential. Our results showed that, while RNAComposer offered a malachite green aptamer 3D structure closer to its crystal structure, the performances of RNAComposer and Rosetta FARFAR2 largely depend upon the secondary structures inputted, and Rosetta FARFAR2 predictions might not even recapitulate the typical, inverted "L" shape tRNA 3D structure. Overall, AlphaFold 3, integrating molecular dynamics principles into its deep learning framework, directly predicted RNA 3D structures from RNA primary sequence inputs, even accepting several common post-transcriptional modifications, which closely aligned with the experimentally determined structures. However, there were significant discrepancies among three computational tools in predicting the distal loop of human pre-microRNA and larger BioRNA (tRNA fused pre-miRNA) molecules whose 3D structures have not been characterized experimentally. While computational predictions show considerable promise, their notable strengths and limitations emphasize the needs for experimental validation of predictions besides characterization of more RNA 3D structures.

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

The role of structure in regulatory RNA elements

Regulatory RNA elements fulfill functions such as translational regulation, control of transcript levels, and regulation of viral genome replication. Trans-acting factors (i.e., RNA-binding proteins) bind the so-called cis elements and confer functionality to the complex. The specificity during protein-RNA complex (RNP) formation often exploits the structural plasticity of RNA. Functional integrity of cis-trans pairs depends on the availability of properly folded RNA elements, and RNA conformational transitions can cause diseases. Knowledge of RNA structure and the conformational space is needed for understanding complex formation and deducing functional effects. However, structure determination of RNAs under in vivo conditions remains challenging. This review provides an overview of structured eukaryotic and viral RNA cis elements and discusses the effect of RNA structural equilibria on RNP formation. We showcase implications of RNA structural changes for diseases, outline strategies for RNA structure-based drug targeting, and summarize the methodological toolbox for deciphering RNA structures.

Viral RNA Interactome: The Ultimate Researcher's Guide to RNA-Protein Interactions

RNA molecules in the cell are bound by a multitude of RNA-binding proteins (RBPs) with a variety of regulatory consequences. Often, interactions with these RNA-binding proteins are facilitated by the complex secondary and tertiary structures of RNA molecules. Viral RNAs especially are known to be heavily structured and interact with many RBPs, with roles including genome packaging, immune evasion, enhancing replication and transcription, and increasing translation efficiency. As such, the RNA-protein interactome represents a critical facet of the viral replication cycle. Characterization of these interactions is necessary for the development of novel therapeutics targeted at the disruption of essential replication cycle events. In this review, we aim to summarize the various roles of RNA structures in shaping the RNA-protein interactome, the regulatory roles of these interactions, as well as up-to-date methods developed for the characterization of the interactome and directions for novel, RNA-directed therapeutics.

Bioorthogonal cyclopropenones for investigating RNA structure

RNA sequences encode secondary and tertiary structures that impact protein production and other cellular processes. Misfolded RNAs can also potentiate disease, but the complete picture is lacking. To establish more comprehensive and accurate RNA structure-function relationships, new methods are needed to interrogate RNA and trap native conformations in cellular environments. Existing tools primarily rely on electrophiles that are constitutively "on" or triggered by UV light, often resulting in high background reactivity. We developed an alternative, chemically triggered approach to crosslink RNAs using bioorthogonal cyclopropenones (CpOs). These reagents selectively react with phosphines to provide ketenes-electrophiles that can trap neighboring nucleophiles to forge covalent crosslinks. As proof-of-concept, we synthesized a panel of CpOs and appended them to thiazole orange (TO-1). The TO-1 conjugates bound selectively to a model RNA aptamer (Mango) with nanomolar affinity, confirmed by fluorescence turn-on. After phosphine administration, covalent crosslinks were formed between the CpO probes and RNA. The degree of crosslinking was both time and dose-dependent. We further applied the chemically triggered tools to model RNAs in biologically relevant conditions. Collectively, this work expands the toolkit of probes for studying RNA and its native conformations.

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

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

TAR RNA selective targeting ruthenium(II) complexes as HIV-1 reverse transcriptase inhibitors: On exploring structure-activity relationships of multiple positions

HIV-1 reverse transcriptase (RT) inhibitors play a crucial role in the treatment of HIV by preventing the activity of the enzyme responsible for the replication of the virus. The HIV-1 Tat protein binds to transactivation response (TAR) RNA and recruits host factors to stimulate HIV-1 transcription. We have created a small library consisting of 4 × 6 polypyridyl Ru(II) complexes that selectively bind to TAR RNA, with targeting groups specific to HIV-1 TAR RNA. The molecule design was conducted by introducing hydroxyl or methoxy groups into an established potent TAR binder. The potential TAR binding ability was analysis from nature charge population and electrostatic potential by quantum chemistry calculations. Key modifications were found to be R1 and R3 groups. The most potent and selective TAR RNA binder was a3 with R1 = OH, R2 = H and R3 = Me. Through molecular recognition of hydrogen bonds and electrostatic attraction, they were able to firmly and selectively bind HIV-1 TAR RNA. Furthermore, they efficiently obstructed the contact between TAR RNA and Tat protein, and inhibited the reverse transcription activity of HIV-1 RT. The polypyridyl Ru(II) complexes were chemical and photo-stable, and sensitive and selective spectroscopic responses to TAR RNA. They exhibited little toxicity towards normal cells. Hence, this study might offer significant drug design approaches for researching AIDS and other illnesses associated with RT, including HCV, EBOV, and SARS-CoV-2. Moreover, it could contribute to fundamental research on the interactions of inorganic transition metal complexes with biomolecules.

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

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

A novel aurone RNA CAG binder inhibits the huntingtin RNA-protein interaction

Huntington's disease (HD) is a devastating, incurable condition whose pathophysiological mechanism relies on mutant RNA CAG repeat expansions. Aberrant recruitment of RNA-binding proteins by mutant CAG hairpins contributes to the progress of neurodegeneration. In this work, we identified a novel binder based on an aurone scaffold that reduces the level of binding of HTT mRNA to the MID1 protein in vitro. The obtained results introduce aurones as a novel platform for the design of functional ligands for disease-related RNA sequences.

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

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

NMR characterization and ligand binding site of the stem-loop 2 motif from the Delta variant of SARS-CoV-2

The stem-loop 2 motif (s2m) in SARS-CoV-2 (SCoV-2) is located in the 3'-UTR. Although s2m has been reported to display characteristics of a mobile genomic element that might lead to an evolutionary advantage, its function has remained unknown. The secondary structure of the original SCoV-2 RNA sequence (Wuhan-Hu-1) was determined by NMR in late 2020, delineating the base-pairing pattern and revealing substantial differences in secondary structure compared to SARS-CoV-1 (SCoV-1). The existence of a single G29742-A29756 mismatch in the upper stem of s2m leads to its destabilization and impedes a complete NMR analysis. With Delta, a variant of concern has evolved with one mutation compared to the original sequence that replaces G29742 by U29742. We show here that this mutation results in a more defined structure at ambient temperature accompanied by a rise in melting temperature. Consequently, we were able to identify >90% of the relevant NMR resonances using a combination of selective RNA labeling and filtered 2D NOESY as well as 4D NMR experiments. We present a comprehensive NMR analysis of the secondary structure, (sub)nanosecond dynamics, and ribose conformation of s2m Delta based on heteronuclear 13C NOE and T 1 measurements and ribose carbon chemical shift-derived canonical coordinates. We further show that the G29742U mutation in Delta has no influence on the druggability of s2m compared to the Wuhan-Hu-1 sequence. With the assignment at hand, we identify the flexible regions of s2m as the primary site for small molecule binding.

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.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Hidden regulators: the emerging roles of lncRNAs in brain development and disease

Long non-coding RNAs (lncRNAs) have emerged as critical players in brain development and disease. These non-coding transcripts, which once considered as "transcriptional junk," are now known for their regulatory roles in gene expression. In brain development, lncRNAs participate in many processes, including neurogenesis, neuronal differentiation, and synaptogenesis. They employ their effect through a wide variety of transcriptional and post-transcriptional regulatory mechanisms through interactions with chromatin modifiers, transcription factors, and other regulatory molecules. Dysregulation of lncRNAs has been associated with certain brain diseases, including Alzheimer's disease, Parkinson's disease, cancer, and neurodevelopmental disorders. Altered expression and function of specific lncRNAs have been implicated with disrupted neuronal connectivity, impaired synaptic plasticity, and aberrant gene expression pattern, highlighting the functional importance of this subclass of brain-enriched RNAs. Moreover, lncRNAs have been identified as potential biomarkers and therapeutic targets for neurological diseases. Here, we give a comprehensive review of the existing knowledge of lncRNAs. Our aim is to provide a better understanding of the diversity of lncRNA structure and functions in brain development and disease. This holds promise for unravelling the complexity of neurodevelopmental and neurodegenerative disorders, paving the way for the development of novel biomarkers and therapeutic targets for improved diagnosis and treatment.

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.

NMR 1H,19F-based screening of the four stem-looped structure 5_SL1-SL4 located in the 5'-untranslated region of SARS-CoV 2 RNA

Development of new antiviral medication against the beta-coronavirus SARS-CoV-2 (SCoV2) is actively being pursued. Both NMR spectroscopy and crystallography as structural screening technologies have been utilised to screen the viral proteome for binding to fragment libraries. Here, we report on NMR screening of elements of the viral RNA genome with two different ligand libraries using 1H-NMR-screening experiments and 1H and 19F NMR-screening experiments for fluorinated compounds. We screened against the 5'-terminal 119 nucleotides located in the 5'-untranslated region of the RNA genome of SCoV2 and further dissected the four stem-loops into its constituent RNA elements to test specificity of binding of ligands to shorter and longer viral RNA stretches. The first library (DRTL-F library) is enriched in ligands binding to RNA motifs, while the second library (DSI-poised library) represents a fragment library originally designed for protein screening. Conducting screens with two different libraries allows us to compare different NMR screening methodologies, describe NMR screening workflows, validate the two different fragment libraries, and derive initial leads for further downstream medicinal chemistry optimisation.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

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.

NIR Light-Activated Mitochondrial RNA Cross-Linking Strategy for H2S Monitoring and Prolonged Colorectal Tumor Imaging

Molecular diffusion and leakage impede the long-term retention of probes/drugs and may cause potential adverse effects in theranostic fields. Spatiotemporally manipulating the organelle-immobilization behavior of probes/drugs for prolonged tumor retention is indispensable to achieving effective cancer diagnosis and therapy. Herein, we propose a rational strategy that could realize near-infrared light-activated ribonucleic acids (RNAs) cross-linking for prolonged tumor retention and simultaneously endogenous hydrogen sulfide (H2S) monitoring in colorectal tumors. Profiting from efficient singlet oxygen (1O2) generation from Cy796 under 808 nm light irradiation, the 1O2-animated furan moiety in Cy796 could covalently cross-link with cytoplasmic RNAs via a cycloaddition reaction and realize organelle immobilization. Subsequently, specific thiolysis of Cy796 assisted with H2S resulted in homologous product Cy644 with reduced 1O2 generation yields and enhanced absolute fluorescence quantum yields (from 7.42 to 27.70%) with blue-shifted absorption and emission, which avoided the molecular oxidation fluorescence quenching effect mediated by 1O2 and validated fluorescence imaging. Furthermore, studies have demonstrated that our proposed strategy possessed adequate capacity for fluorescence imaging and endogenous H2S detection in HCT116 cells, particularly accumulated at the tumor sites, and retained long-term imaging with excellent biocompatibility. The turn-on fluorescence mode and turn-off 1O2 generation efficiency in our strategy successfully realized a diminished fluorescence cross-talk and oxidation quenching effect. It is adequately envisioned that our proposed strategy for monitoring biomarkers and prolonged tumor retention will contribute tremendous dedication in the clinical, diagnostic, and therapeutic fields.

Using the structural diversity of RNA: protein interfaces to selectively target RNA with small molecules in cells: methods and perspectives

In recent years, RNA has gained traction both as a therapeutic molecule and as a therapeutic target in several human pathologies. In this review, we consider the approach of targeting RNA using small molecules for both research and therapeutic purposes. Given the primary challenge presented by the low structural diversity of RNA, we discuss the potential for targeting RNA: protein interactions to enhance the structural and sequence specificity of drug candidates. We review available tools and inherent challenges in this approach, ranging from adapted bioinformatics tools to in vitro and cellular high-throughput screening and functional analysis. We further consider two critical steps in targeting RNA/protein interactions: first, the integration of in silico and structural analyses to improve the efficacy of molecules by identifying scaffolds with high affinity, and second, increasing the likelihood of identifying on-target compounds in cells through a combination of high-throughput approaches and functional assays. We anticipate that the development of a new class of molecules targeting RNA: protein interactions to prevent physio-pathological mechanisms could significantly expand the arsenal of effective therapeutic compounds.

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.

An Encodable Scaffold for Sequence-Specific Recognition of Duplex RNA

RNA, unlike DNA, folds into a multitude of secondary and tertiary structures. This structural diversity has impeded the development of ligands that can sequence-specifically target this biomolecule. We sought to develop ligands for double-stranded RNA (dsRNA) segments, which are ubiquitous in RNA tertiary structure. The major groove of double-stranded DNA is sequence-specifically recognized by a range of dimeric helical transcription factors, including the basic leucine zippers (bZIP) and basic helix-loop-helix (bHLH) proteins; however, such simple structural motifs are not prevalent in RNA-binding proteins. We interrogated the high-resolution structures of DNA and RNA to identify requirements for a helix fork motif to occupy dsRNA major grooves akin to dsDNA. Our analysis suggested that the rigidity and angle of approach of dimeric helices in bZIP/bHLH motifs are not ideal for the binding of dsRNA major grooves. This investigation revealed that the replacement of the leucine zipper motifs in bHLH proteins with synthetic crosslinkers would allow recognition of dsRNA. We show that a model bHLH DNA-binding motif does not bind dsRNA but can be reengineered as an RNA ligand. Based on this hypothesis, we rationally designed a miniature synthetic crosslinked helix fork (CHF) as a generalizable proteomimetic scaffold for targeting dsRNA. We evaluated several CHF constructs against a set of RNA and DNA hairpins to probe the specificity of the designed construct. Our studies reveal a new class of proteomimetics as an encodable platform for sequence-specific recognition of dsRNA.

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.

Proximity-inducing modalities: the past, present, and future

Living systems use proximity to regulate biochemical processes. Inspired by this phenomenon, bifunctional modalities that induce proximity have been developed to redirect cellular processes. An emerging example of this class is molecules that induce ubiquitin-dependent proteasomal degradation of a protein of interest, and their initial development sparked a flurry of discovery for other bifunctional modalities. Recent advances in this area include modalities that can change protein phosphorylation, glycosylation, and acetylation states, modulate gene expression, and recruit components of the immune system. In this review, we highlight bifunctional modalities that perform functions other than degradation and have great potential to revolutionize disease treatment, while also serving as important tools in basic research to explore new aspects of biology.

Cardiac splicing as a diagnostic and therapeutic target

Despite advances in therapeutics for heart failure and arrhythmias, a substantial proportion of patients with cardiomyopathy do not respond to interventions, indicating a need to identify novel modifiable myocardial pathobiology. Human genetic variation associated with severe forms of cardiomyopathy and arrhythmias has highlighted the crucial role of alternative splicing in myocardial health and disease, given that it determines which mature RNA transcripts drive the mechanical, structural, signalling and metabolic properties of the heart. In this Review, we discuss how the analysis of cardiac isoform expression has been facilitated by technical advances in multiomics and long-read and single-cell sequencing technologies. The resulting insights into the regulation of alternative splicing - including the identification of cardiac splice regulators as therapeutic targets and the development of a translational pipeline to evaluate splice modulators in human engineered heart tissue, animal models and clinical trials - provide a basis for improved diagnosis and therapy. Finally, we consider how the medical and scientific communities can benefit from facilitated acquisition and interpretation of splicing data towards improved clinical decision-making and patient care.

Spinal Muscular Atrophy: The Past, Present, and Future of Diagnosis and Treatment

Spinal muscular atrophy (SMA) is a lower motor neuron disease with autosomal recessive inheritance. The first cases of SMA were reported by Werdnig in 1891. Although the phenotypic variation of SMA led to controversy regarding the clinical entity of the disease, the genetic homogeneity of SMA was proved in 1990. Five years later, in 1995, the gene responsible for SMA, SMN1, was identified. Genetic testing of SMN1 has enabled precise epidemiological studies, revealing that SMA occurs in 1 of 10,000 to 20,000 live births and that more than 95% of affected patients are homozygous for SMN1 deletion. In 2016, nusinersen was the first drug approved for treatment of SMA in the United States. Two other drugs were subsequently approved: onasemnogene abeparvovec and risdiplam. Clinical trials with these drugs targeting patients with pre-symptomatic SMA (those who were diagnosed by genetic testing but showed no symptoms) revealed that such patients could achieve the milestones of independent sitting and/or walking. Following the great success of these trials, population-based newborn screening programs for SMA (more precisely, SMN1-deleted SMA) have been increasingly implemented worldwide. Early detection by newborn screening and early treatment with new drugs are expected to soon become the standards in the field of SMA.

Novel roles of karyopherin subunit alpha 2 in hepatocellular carcinoma

Hepatocellular carcinoma is the most common type of liver cancer and associated with a high fatality rate. This disease poses a major threat to human health worldwide. A considerable number of genetic and epigenetic factors are involved in the development of hepatocellular carcinoma. However, the molecular mechanism underlying the progression of hepatocellular carcinoma remains unclear. Karyopherin subunit alpha 2 (KPNA2), also termed importin α1, is a member of the nuclear transporter family. In recent years, KPNA2 has been gradually linked to the nuclear transport pathway for a variety of tumor-associated proteins. Furthermore, it promotes tumor development by participating in various pathophysiological processes such as cell proliferation, apoptosis, immune response, and viral infection. In hepatocellular carcinoma, it has been found that KPNA2 expression is significantly higher in liver cancer tissues versus paracancerous tissues. Moreover, it has been identified as a marker of poor prognosis and early recurrence in patients with hepatocellular carcinoma. Nevertheless, the role of KPNA2 in the development of hepatocellular carcinoma remains to be determined. This review summarizes the current knowledge on the pathogenesis and role of KPNA2 in hepatocellular carcinoma, and provides new directions and strategies for the diagnosis, treatment, and prediction of prognosis of this disease.

The Functional RNA Identification (FRID) Pipeline: Identification of Potential Pseudoknot-Containing RNA Elements as Therapeutic Targets for SARS-CoV-2

The COVID-19 pandemic persists despite the development of effective vaccines. As such, it remains crucial to identify new targets for antiviral therapies. The causative virus of COVID-19, SARS-CoV-2, is a positive-sense RNA virus with RNA structures that could serve as therapeutic targets. One such RNA with established function is the frameshift stimulatory element (FSE), which promotes programmed ribosomal frameshifting. To accelerate identification of additional functional RNA elements, we introduce a novel computational approach termed the Functional RNA Identification (FRID) pipeline. The guiding principle of our pipeline, which uses established component programs as well as customized component programs, is that functional RNA elements have conserved secondary and pseudoknot structures that facilitate function. To assess the presence and conservation of putative functional RNA elements in SARS-CoV-2, we compared over 6,000 SARS-CoV-2 genomic isolates. We identified 22 functional RNA elements from the SARS-CoV-2 genome, 14 of which have conserved pseudoknots and serve as potential targets for small molecule or antisense oligonucleotide therapeutics. The FRID pipeline is general and can be applied to identify pseudoknotted RNAs for targeted therapeutics in genomes or transcriptomes from any virus or organism.

How does precursor RNA structure influence RNA processing and gene expression?

RNA is a fundamental biomolecule that has many purposes within cells. Due to its single-stranded and flexible nature, RNA naturally folds into complex and dynamic structures. Recent technological and computational advances have produced an explosion of RNA structural data. Many RNA structures have regulatory and functional properties. Studying the structure of nascent RNAs is particularly challenging due to their low abundance and long length, but their structures are important because they can influence RNA processing. Precursor RNA processing is a nexus of pathways that determines mature isoform composition and that controls gene expression. In this review, we examine what is known about human nascent RNA structure and the influence of RNA structure on processing of precursor RNAs. These known structures provide examples of how other nascent RNAs may be structured and show how novel RNA structures may influence RNA processing including splicing and polyadenylation. RNA structures can be targeted therapeutically to treat disease.

There's more to enzyme antagonism than inhibition

A native enzyme's usual assurance in recognizing their physiological substrate(s) at the ground state and on going to the transition state can be undermined by interactions with selected small molecule antagonists, leading to the generation of abnormal products. We classify this mode of enzyme antagonism resulting in the gain-of-nonnative-function as paracatalytic induction. Enzymes bound by paracatalytic inducers exhibit new or enhanced activity toward transformations that appear aberrant or erroneous. The enzyme/ paracatalytic inducer complex may take up native substrate but then bring about a transformation that is chemically distinct from the normal reaction. Alternatively, the enzyme / paracatalytic inducer complex may exhibit abnormal ground state selectivity, preferentially interacting with and transforming a molecule outside the physiological substrate scope. Paracatalytic inducers can be cytotoxic, while in other cases they divert enzyme activity toward transformations that appear adaptive and even therapeutically useful. In this perspective, we highlight two noteworthy examples from recent literature.

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.

Chemical RNA Cross-Linking: Mechanisms, Computational Analysis, and Biological Applications

In recent years, RNA has emerged as a multifaceted biomolecule that is involved in virtually every function of the cell and is critical for human health. This has led to a substantial increase in research efforts to uncover the many chemical and biological aspects of RNA and target RNA for therapeutic purposes. In particular, analysis of RNA structures and interactions in cells has been critical for understanding their diverse functions and druggability. In the last 5 years, several chemical methods have been developed to achieve this goal, using chemical cross-linking combined with high-throughput sequencing and computational analysis. Applications of these methods resulted in important new insights into RNA functions in a variety of biological contexts. Given the rapid development of new chemical technologies, a thorough perspective on the past and future of this field is provided. In particular, the various RNA cross-linkers and their mechanisms, the computational analysis and challenges, and illustrative examples from recent literature are discussed.

Selective Chemical Modification to the Higher-Order Structures of Nucleic Acids

DNA and RNA can adopt a variety of stable higher-order structural motifs, including G-quadruplex (G4 s), mismatches, and bulges. Many of these secondary structures are closely related to the regulation of gene expression. Therefore, the higher-order structure of nucleic acids is one of the candidate therapeutic targets, and the development of binding molecules targeting the higher-order structure of nucleic acids has been pursued vigorously. Furthermore, as one of the methodologies for detecting the higher-order structures of these nucleic acids, developing techniques for the selective chemical modification of the higher-order structures of nucleic acids is also underway. In this personal account, we focus on the following higher-order structures of nucleic acids, double-stranded DNA containing the abasic site, T-T/U-U mismatch structure, and G-quadruplex structure, and describe the development of molecules that bind to and chemically modify these structures.

Application of Molecular Dynamics to Expand Docking Program's Exploratory Capabilities and to Evaluate Its Predictions

Recognition of the growing importance of RNA as a target for therapeutic or diagnostic ligands brings the importance of computational predictions of docking poses to such receptors to the forefront. Most docking programs have been optimized for protein targets, based on a relatively rich pool of known docked protein structures. Unfortunately, despite progress, numbers of known docked RNA complexes are low and the accuracy of the computational predictions trained on those inadequate samples lags behind that achieved for proteins. Compared to proteins, RNA structures generally have fewer docking pockets, have less diverse electrostatic surfaces, and are more flexible, raising the possibility of producing only transiently available good docking targets. We are presenting a docking prediction protocol that adds molecular dynamics simulations before and after the actual docking in order to explore the conformational space of the target RNA and then to reevaluate the stability of the predicted RNA-ligand complex. In this way we are attempting to overcome important limitations of the docking programs: the rigid (fully or mostly) target structure and imperfect nature of the docking scoring functions.

RNAi-Based Therapeutics and Novel RNA Bioengineering Technologies

RNA interference (RNAi) provides researchers with a versatile means to modulate target gene expression. The major forms of RNAi molecules, genome-derived microRNAs (miRNAs) and exogenous small interfering RNAs (siRNAs), converge into RNA-induced silencing complexes to achieve posttranscriptional gene regulation. RNAi has proven to be an adaptable and powerful therapeutic strategy where advancements in chemistry and pharmaceutics continue to bring RNAi-based drugs into the clinic. With four siRNA medications already approved by the US Food and Drug Administration (FDA), several RNAi-based therapeutics continue to advance to clinical trials with functions that closely resemble their endogenous counterparts. Although intended to enhance stability and improve efficacy, chemical modifications may increase risk of off-target effects by altering RNA structure, folding, and biologic activity away from their natural equivalents. Novel technologies in development today seek to use intact cells to yield true biologic RNAi agents that better represent the structures, stabilities, activities, and safety profiles of natural RNA molecules. In this review, we provide an examination of the mechanisms of action of endogenous miRNAs and exogenous siRNAs, the physiologic and pharmacokinetic barriers to therapeutic RNA delivery, and a summary of the chemical modifications and delivery platforms in use. We overview the pharmacology of the four FDA-approved siRNA medications (patisiran, givosiran, lumasiran, and inclisiran) as well as five siRNAs and several miRNA-based therapeutics currently in clinical trials. Furthermore, we discuss the direct expression and stable carrier-based, in vivo production of novel biologic RNAi agents for research and development. SIGNIFICANCE STATEMENT: In our review, we summarize the major concepts of RNA interference (RNAi), molecular mechanisms, and current state and challenges of RNAi drug development. We focus our discussion on the pharmacology of US Food and Drug Administration-approved RNAi medications and those siRNAs and miRNA-based therapeutics that entered the clinical investigations. Novel approaches to producing new true biological RNAi molecules for research and development are highlighted.

ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes

A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.

Targeting RNA structures with small molecules

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

Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome

RNA plays vital functional roles in almost every component of biology, and these functional roles are often influenced by its folding into secondary and tertiary structures. An important role of RNA secondary structure is in maintaining proper gene regulation; therefore, making accurate predictions of the structures involved in these processes is important. In this study, we have expanded on our previous work that led to the creation of the RNAStructuromeDB. Unlike this previous study that analyzed the human genome at low resolution, we have now scanned the protein-coding human transcriptome at high (single nt) resolution. This provides more robust structure predictions for over 100,000 isoforms of known protein-coding genes. Notably, we also utilize the motif identification tool, ScanFold, to model structures with high propensity for ordered/evolved stability. All data have been uploaded to the RNAStructuromeDB, allowing for easy searching of transcripts, visualization of data tracks (via the Integrative Genomics Viewer or IGV), and download of ScanFold data—including unique highly-ordered motifs. Herein, we provide an example analysis of MAT2A to demonstrate the utility of ScanFold at finding known and novel secondary structures, highlighting regions of potential functionality, and guiding generation of functional hypotheses through use of the data.

Scorpion-Shaped Zinc Porphyrins as Tetrafunctional TAR RNA Predators and HIV-1 Reverse Transcriptase Inhibitors

HIV-1 reverse transcriptase (RT) inhibitors are fundamental to the discovery and development of anti-HIV drugs. Their main target is RT, and only a tiny number of them can bind to viral RNA. In this paper, five new Zn(II) porphyrin compounds were developed with different characters. ZnTPP4 has both the appearance and the functions of a scorpion with a rigid tail and stinger to selectively hunt HIV-1 TAR RNA based on the molecular recognition of hydrogen bonds, a fierce chelicera to bite RNA by metal coordination, mighty pedipalps to grasp the bound RNA by supramolecular inclusion, and a broad body maintaining the configuration of each functional area so that they can cooperate with each other and providing accommodation space for the bound RNA. This tetrafunctional Zn(II) porphyrin is relatively nontoxic to normal cells and can produce sensitive responses for RNA. Moreover, this work offers practical construction methodologies for medication of AIDS and other diseases closely related to RT like EBOV and SARS-CoV-2.

Beyond Proteolysis-Targeting Chimeric Molecules: Designing Heterobifunctional Molecules Based on Functional Effectors

In recent years, with the successful development of proteolysis-targeting chimeric molecules (PROTACs), the potential of heterobifunctional molecules to contribute to reenvisioning drug design, especially small-molecule drugs, has been increasingly recognized. Inspired by PROTACs, diverse heterobifunctional molecules have been reported to simultaneously bind two or more molecules and bring them into proximity to interaction, such as ribonuclease-recruiting, autophagy-recruiting, lysosome-recruiting, kinase-recruiting, phosphatase-recruiting, glycosyltransferase-recruiting, and acetyltransferase-recruiting chimeras. On the basis of the heterobifunctional principle, more opportunities for advancing drug design by linking potential effectors to a protein of interest (POI) have emerged. Herein, we introduce heterobifunctional molecules other than PROTACs, summarize the limitations of existing molecules, list the main challenges, and propose perspectives for future research directions, providing insight into alternative design strategies based on substrate-proximity-based targeting.

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.