Principles for targeting RNA with drug-like small molecules

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

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

Low-Molecular Weight Small Molecules Can Potently Bind RNA and Affect Oncogenic Pathways in Cells

RNA is challenging to target with bioactive small molecules, particularly those of low molecular weight that bind with sufficient affinity and specificity. In this report, we developed a platform to address this challenge, affording a novel bioactive interaction. An RNA-focused small-molecule fragment collection (n = 2500) was constructed by analyzing features in all publicly reported compounds that bind RNA, the largest collection of RNA-focused fragments to date. The RNA-binding landscape for each fragment was studied by using a library-versus-library selection with an RNA library displaying a discrete structural element, probing over 12.8 million interactions, the greatest number of interactions between fragments and biomolecules probed experimentally. Mining of this dataset across the human transcriptome defined a drug-like fragment that potently and specifically targeted the microRNA-372 hairpin precursor, inhibiting its processing into the mature, functional microRNA and alleviating invasive and proliferative oncogenic phenotypes in gastric cancer cells. Importantly, this fragment has favorable properties, including an affinity for the RNA target of 300 ± 130 nM, a molecular weight of 273 Da, and quantitative estimate of drug-likeness (QED) score of 0.8. (For comparison, the mean QED of oral medicines is 0.6 ± 0.2). Thus, these studies demonstrate that a low-molecular weight, fragment-like compound can specifically and potently modulate RNA targets.

Modulating biomolecular condensates: a novel approach to drug discovery

In the past decade, membraneless assemblies known as biomolecular condensates have been reported to play key roles in many cellular functions by compartmentalizing specific proteins and nucleic acids in subcellular environments with distinct properties. Furthermore, growing evidence supports the view that biomolecular condensates often form by phase separation, in which a single-phase system demixes into a two-phase system consisting of a condensed phase and a dilute phase of particular biomolecules. Emerging understanding of condensate function in normal and aberrant cellular states, and of the mechanisms of condensate formation, is providing new insights into human disease and revealing novel therapeutic opportunities. In this Perspective, we propose that such insights could enable a previously unexplored drug discovery approach based on identifying condensate-modifying therapeutics (c-mods), and we discuss the strategies, techniques and challenges involved.

MicroRNAs modulate neuroinflammation after intracerebral hemorrhage: Prospects for new therapy

Intracerebral hemorrhage (ICH) is the most common subtype of hemorrhagic stroke. After ICH, blood components extravasate from vessels into the brain, activating immune cells and causing them to release a series of inflammatory mediators. Immune cells, together with inflammatory mediators, lead to neuroinflammation in the perihematomal region and the whole brain, and neuroinflammation is closely related to secondary brain injury as well as functional recovery of the brain. Despite recent progress in understanding the pathophysiology of ICH, there is still no effective treatment for this disease. MicroRNAs (miRNAs) are non-coding RNAs 17-25 nucleotides in length that are generated naturally in the human body. They bind complementarily to messenger RNAs and suppress translation, thus regulating gene expression at the post-transcriptional level. They have been found to regulate the pathophysiological process of ICH, particularly the neuroinflammatory cascade. Multiple preclinical studies have shown that manipulating the expression and activity of miRNAs can modulate immune cell activities, influence neuroinflammatory responses, and ultimately affect neurological functions after ICH. This implicates the potentially crucial roles of miRNAs in post-ICH neuroinflammation and indicates the possibility of applying miRNA-based therapeutics for this disease. Thus, this review aims to address the pathophysiological roles and molecular underpinnings of miRNAs in the regulation of neuroinflammation after ICH. With a more sophisticated understanding of ICH and miRNAs, it is possible to translate these findings into new pharmacological therapies for ICH.

Binding-driven reactivity attenuation enables NMR identification of selective drug candidates for nucleic acid targets

NMR methods, and in particular ligand-based approaches, are among the most robust and reliable alternatives for binding detection and consequently, they have become highly popular in the context of hit identification and drug discovery. However, when dealing with DNA/RNA targets, these techniques face limitations that have precluded widespread application in medicinal chemistry. In order to expand the arsenal of spectroscopic tools for binding detection and to overcome the existing difficulties, herein we explore the scope and limitations of a strategy that makes use of a binding indicator previously unexploited by NMR: the perturbation of the ligand reactivity caused by complex formation. The obtained results indicate that ligand reactivity can be utilised to reveal association processes and identify the best binders within mixtures of significant complexity, providing a conceptually different reactivity-based alternative within NMR screening methods.

Biofilm Formation in Streptococcus agalactiae Is Inhibited by a Small Regulatory RNA Regulated by the Two-Component System CiaRH

Regulatory small RNAs (sRNAs) are involved in the adaptation of bacteria to their environment. CiaR-dependent sRNAs (csRNAs) are controlled by the regulatory two-component system (TCS) CiaRH, which is widely conserved in streptococci. Except for Streptococcus pneumoniae and Streptococcus sanguinis, the targets of these csRNAs have not yet been investigated. Streptococcus agalactiae, the leading cause of neonatal infections, has four conserved csRNA genes, namely, srn015, srn024, srn070, and srn085. Here, we demonstrate the importance of the direct repeat TTTAAG-N5-TTTAAG in the regulation of these csRNAs by CiaRH. A 24-nucleotide Srn024-sap RNA base-pairing region is predicted in silico. The sap gene encodes a LPXTG-cell wall-anchored pullulanase. This protein cleaves α-glucan polysaccharides such as pullulan and glycogen present in the environment to release glucose and is involved in adhesion to human cervical epithelial cells. Inactivation of S. agalactiae pullulanase (SAP) leads to no bacterial growth in a medium with only pullulan as a carbon source and reduced biofilm formation, while deletion of ciaRH and srn024 genes significantly increases bacterial growth and biofilm formation. Using a new translational fusion vector, we demonstrated that Srn024 is involved in the posttranscriptional regulation of sap expression. Complementary base pair exchanges in S. agalactiae suggest that Srn024 interacts directly with sap mRNA and that disruption of this RNA pairing is sufficient to yield the biofilm phenotype of Srn024 deletion. These results suggest the involvement of Srn024 in the adaptation of S. agalactiae to environmental changes and biofilm formation, likely through the regulation of the sap gene. IMPORTANCE Although Streptococcus agalactiae is a commensal bacterium of the human digestive and genitourinary tracts, it is also an opportunistic pathogen for humans and other animals. As the main cause of neonatal infections, it is responsible for pneumonia, bacteremia, and meningitis. However, its adaptation to these different ecological niches is not fully understood. Bacterial regulatory networks are involved in this adaptation, and the regulatory TCSs (e.g., CiaRH), as well as the regulatory sRNAs, are part of it. This study is the first step to understand the role of csRNAs in the adaptation of S. agalactiae. This bacterium does not currently exhibit extensive antibiotic resistance. However, it is crucial to find alternatives before multidrug resistance emerges. Therefore, we propose that drugs targeting regulatory RNAs with Srn024-like activities would affect pathogens by reducing their abilities to form biofilm and to adapt to host niches.

A comprehensive review of methods to study lncRNA-protein interactions in solution

The long non-coding RNAs (lncRNAs) other than rRNA and tRNA were earlier assumed to be 'junk genomic material'. However, recent advancements in genomics methods have highlighted their roles not only in housekeeping but also in the progression of diseases like cancer as well as viral infections. lncRNAs owing to their length, have both short-range and long-range interactions resulting in complex folded structures that recruit various biomolecules enabling lncRNAs to undertake their various biological functions. Using cell lysate pull-down assays increasing number of lnRNAs-interacting proteins are being identified. These interactions can be further exploited to develop targeted novel therapeutic strategies to inhibit lncRNA-protein interactions. This review attempts to succinctly techniques that can identify and characterize the lnRNAs-protein interactions (i.e. affinity, stoichiometry, and thermodynamics). Furthermore, using other sophisticated biophysical techniques, one can also perform size estimations, and determine low-resolution structures. Since these methods study the biomolecules in solution, large-scale structural observations can be performed in real-time. This review attempts to briefly introduce the readers to biochemical and biophysical techniques, such that they can utilize these methods to obtain a holistic characterization of the biomolecules of interest. Additionally, it should be noted that the use of these methods is not limited to the characterization of the interacting molecules but can also be used to determine the efficacy of the therapeutic molecules to disrupt these interactions.

Analysis of key genes in Mycobacterium ulcerans reveals conserved RNA structural motifs and regions with apparent pressure to remain unstructured

Buruli Ulcer is a neglected tropical disease that results in disfiguring and dangerous lesions in affected persons across a wide geographic area, including much of West Africa. The causative agent of Buruli Ulcer is Mycobacterium ulcerans, a relative of the bacterium that causes tuberculosis and leprosy. Few therapeutic options exist for the treatment of this disease beyond antibiotics in the early stages, which are frequently ineffective, and surgical removal in the later stage. In this study we analyze six genes in Mycobacterium ulcerans that have high potential of therapeutic targeting. We focus our analysis on a combined in silico and comparative sequence study of potential RNA secondary structure across these genes. The result of this work was the comprehensive local RNA structural landscape across each of these significant genes. This revealed multiple sites of ordered and evolved RNA structure interspersed between sequences that either have no bias for structure or, indeed, appear to be ordered to be unstructured and (potentially) accessible. In addition to providing data that could be of interest to basic biology, our results provide guides for efforts aimed at targeting this pathogen at the RNA level. We explore this latter possibility through the in silico analysis of antisense oligonucleotides that could potentially be used to target pathogen RNA.

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.

Global 5'-UTR RNA structure regulates translation of a SERPINA1 mRNA

SERPINA1 mRNAs encode the protease inhibitor α-1-antitrypsin and are regulated through post-transcriptional mechanisms. α-1-antitrypsin deficiency leads to chronic obstructive pulmonary disease (COPD) and liver cirrhosis, and specific variants in the 5'-untranslated region (5'-UTR) are associated with COPD. The NM_000295.4 transcript is well expressed and translated in lung and blood and features an extended 5'-UTR that does not contain a competing upstream open reading frame (uORF). We show that the 5'-UTR of NM_000295.4 folds into a well-defined multi-helix structural domain. We systematically destabilized mRNA structure across the NM_000295.4 5'-UTR, and measured changes in (SHAPE quantified) RNA structure and cap-dependent translation relative to a native-sequence reporter. Surprisingly, despite destabilizing local RNA structure, most mutations either had no effect on or decreased translation. Most structure-destabilizing mutations retained native, global 5'-UTR structure. However, those mutations that disrupted the helix that anchors the 5'-UTR domain yielded three groups of non-native structures. Two of these non-native structure groups refolded to create a stable helix near the translation initiation site that decreases translation. Thus, in contrast to the conventional model that RNA structure in 5'-UTRs primarily inhibits translation, complex folding of the NM_000295.4 5'-UTR creates a translation-optimized message by promoting accessibility at the translation initiation site.

Multiassay Profiling of a Focused Small Molecule Library Reveals Predictive Bidirectional Modulation of the lncRNA MALAT1 Triplex Stability In Vitro

The rapidly accelerating characterization of RNA tertiary structures has revealed their pervasiveness and active roles in human diseases. Small molecule-mediated modulation of RNA tertiary structures constitutes an attractive avenue for the development of tools for therapeutically targeting and/or uncovering the pathways associated with these RNA motifs. This potential has been highlighted by targeting of the triple helix present at the 3'-end of the noncoding RNA MALAT1, a transcript implicated in several human diseases. This triplex has been reported to decrease the susceptibility of the transcript to degradation and promote its cellular accumulation. While small molecules have been shown to bind to and impact the stability of the MALAT1 triple helix, the small molecule properties that lead to these structural modulations are not well understood. We designed a library utilizing the diminazene scaffold, which is underexplored but precedented for nucleic acid binding, to target the MALAT1 triple helix. We employed multiple assays to holistically assess what parameters, if any, could predict the small molecule affinity and effect on triplex stability. We designed and/or optimized competition, calorimetry, and thermal shift assays as well as an enzymatic degradation assay, the latter of which led to the discovery of bidirectional modulators of triple helix stability within the scaffold-centric library. Determination of quantitative structure-activity relationships afforded predictive models for both affinity- and stability-based assays. This work establishes a suite of powerful orthogonal biophysical tools for the evaluation of small molecule:RNA triplex interactions that generate predictive models and will allow small molecule interrogation of the growing body of disease-associated RNA triple helices.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

Current knowledge on the tissue distribution of mRNA nanocarriers for therapeutic protein expression

Exogenously delivered mRNA-based drugs are emerging as a new class of therapeutics with the potential to treat several diseases. Over the last decade, advancements in the design of non-viral delivery tools have enabled mRNA to be evaluated for several therapeutic purposes including protein replacement therapies, gene editing, and vaccines. However, in vivo delivery of mRNA to targeted organs and cells remains a critical challenge. Evaluation of the biodistribution of mRNA vehicles is of utmost importance for the development of effective pharmaceutical candidates. In this review, we discuss the recent advances in the design of nanoparticles loaded with mRNA and extrapolate the key factors influencing their biodistribution following administration. Finally, we highlight the latest developments in the preclinical and clinical translation of mRNA therapeutics for protein supplementation therapy.

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

MOTIVATION

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 'undruggable' protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that have hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule (RNA-SM) recognition.

RESULTS

Here, we present Harnessing RIBOnucleic acid-Small molecule Structures (HARIBOSS), a curated collection of RNA-SM structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and ultimately the development of in silico strategies to identify RNA-targeting small molecules.

AVAILABILITY AND IMPLEMENTATION

HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Slow Dynamic RNA Switch Regulates Processing of microRNA-21

The microRNAs are non-coding RNAs which post-transcriptionally regulate the expression of many eukaryotic genes, and whose dysregulation is a driver of human disease. Here we report the discovery of a very slow (0.1 s-1) conformational rearrangement at the Dicer cleavage site of pre-miR-21, which regulates the relative concentration of readily- and inefficiently-processed RNA structural states. We show that this dynamic switch is affected by single nucleotide mutations and can be biased by small molecule and peptide ligands, which can direct the microRNA to occupy the inefficiently processed state and reduce processing efficiency. This result reveals a new mechanism of RNA regulation and suggests a chemical approach to suppressing or activating pathogenic microRNAs by selective stabilization of their unprocessed or processed states.

Quantum Chemical Calculations with Machine Learning for Multipolar Electrostatics Prediction in RNA: An Application to Pentose

To develop a realistic electrostatic model that allows for the anisotropy of the atomic electron density, high-rank atomic multipole moments computed by quantum chemical calculations have been studied extensively. However, it is hard to process huge RNA systems only relying on quantum chemical calculations due to its highly computational cost. In this study, we employ five machine learning methods of Gaussian process regression with automatic relevance determination (ARDGPR), Kriging, radial basis function neural networks, Bagging, and generalized regression neural network to predict atomic multipole moments. Atom-atom electrostatic interaction energies are subsequently computed using the predicted atomic multipole moments in the pilot system pentose of RNA. Here, the performance of the five methods is compared in terms of both the multipole moment prediction errors and the electrostatic energy prediction errors. For the predicted high-rank multipole moments of the four elements (O, C, N, and H) in capped pentose, ARDGPR and Kriging consistently outperform the other three methods. Therefore, the multipole moments predicted by the two best methods of ARDGPR and Kriging are then used to predict electrostatic interaction energy of each pentose. Finally, the absolute average energy errors of ARDGPR and Kriging are 1.83 and 4.33 kJ mol^-1, respectively. Compared to Kriging, the ARDGPR method achieves a 58% decrease in the absolute average energy error. These satisfactory results demonstrated that the ARDGPR method with the strong feature extraction ability can predict the electrostatic interaction energy of pentose in RNA correctly and reliably.

An in vitro cytomimetic of in-cell RNA folding

To discover the cytomimetic that accounts for cytoplasmic crowding and sticking on RNA stability, we conducted a two-dimensional scan of mixtures of artificial crowding and sticking agents, PEG10k and M-PERTM. As our model RNA, we investigate the fourU RNA thermometer motif of Salmonella, a hairpin-structured RNA that regulates translation by unfolding and exposing its RBS in response to temperature perturbations. We found that the addition of artificial crowding and sticking agents leads to a stabilization and destabilization of RNA folding, respectively, through the excluded volume effect and surface interactions. FRET-labels were added to the fourU RNA and Fast Relaxation Imaging (FReI), fluorescence microscopy coupled to temperature-jump spectroscopy, probed differences between folding stability of RNA inside single living cells and in vitro. Our results suggest that the cytoplasmic environment affecting RNA folding is comparable to a combination of 20% v/v M-PERTM and 150 g/L PEG10k.

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

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

Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses

Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world.

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

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

Nucleic-Acid-Based Targeted Degradation in Drug Discovery

Targeted protein degradation (TPD), represented by proteolysis-targeting chimera (PROTAC), has emerged as a novel therapeutic modality in drug discovery. However, the application of conventional PROTACs is limited to protein targets containing cytosolic domains with ligandable sites. Recently, nucleic-acid-based modalities, such as modified oligonucleotide mimics and aptamers, opened new avenues to degrade protein targets and greatly expanded the scope of TPD. Beyond constructing protein-degrading chimeras, nucleic acid motifs can also serve as substrates for targeted degradation. Particularly, the new type of chimeric RNA degrader termed ribonuclease-targeting chimera (RIBOTAC) has shown promising features in drug discovery. Here, we provide an overview of the newly emerging TPD strategies based on nucleic acids as well as new strategies for targeted degradation of nucleic acid (RNA) targets. The design strategies, case studies, potential applications, and challenges are focused on.

RNA-based therapeutics: an overview and prospectus

The growing understanding of RNA functions and their crucial roles in diseases promotes the application of various RNAs to selectively function on hitherto "undruggable" proteins, transcripts and genes, thus potentially broadening the therapeutic targets. Several RNA-based medications have been approved for clinical use, while others are still under investigation or preclinical trials. Various techniques have been explored to promote RNA intracellular trafficking and metabolic stability, despite significant challenges in developing RNA-based therapeutics. In this review, the mechanisms of action, challenges, solutions, and clinical application of RNA-based therapeutics have been comprehensively summarized.

[Opportunities and Challenges of RNA Interference Therapeutics in Oncology]

As the discovery of RNA interference (RNAi) and the gradual conquering of a series of technical issues, a few of RNAi therapeutics have been approved in the non-tumor field abroad. With the advantages of high specificity, long duration of efficacy, and high success rate of development, RNAi therapeutics have become the emerging field globally. There are no RNAi therapeutics approved in oncology so far, and people are hoping a breakthrough in the field. In the present article, the characteristics and potential anti-tumor mechanism of RNAi therapeutics, difficulties in delivery system and progress in oncology are described, and the potential reasons why their success in non-tumor field is difficult to be simply replicated in tumor field are analyzed, providing reference for research and clinical transformation of RNAi therapeutics in oncology.
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Geneticin shows selective antiviral activity against SARS-CoV-2 by interfering with programmed -1 ribosomal frameshifting

SARS-CoV-2 is currently causing an unprecedented pandemic. While vaccines are massively deployed, we still lack effective large-scale antiviral therapies. In the quest for antivirals targeting conserved structures, we focused on molecules able to bind viral RNA secondary structures. Aminoglycosides are a class of antibiotics known to interact with the ribosomal RNA of both prokaryotes and eukaryotes and have previously been shown to exert antiviral activities by interacting with viral RNA. Here we show that the aminoglycoside geneticin is endowed with antiviral activity against all tested variants of SARS-CoV-2, in different cell lines and in a respiratory tissue model at non-toxic concentrations. The mechanism of action is an early inhibition of RNA replication and protein expression related to a decrease in the efficiency of the -1 programmed ribosomal frameshift (PRF) signal of SARS-CoV-2. Using in silico modelling, we have identified a potential binding site of geneticin in the pseudoknot of frameshift RNA motif. Moreover, we have selected, through virtual screening, additional RNA binding compounds, interacting with the same site with increased potency.

An LNA-amide modification that enhances the cell uptake and activity of phosphorothioate exon-skipping oligonucleotides

Oligonucleotides that target mRNA have great promise as therapeutic agents for life-threatening conditions but suffer from poor bioavailability, hence high cost. As currently untreatable diseases come within the reach of oligonucleotide therapies, new analogues are urgently needed to address this. With this in mind we describe reduced-charge oligonucleotides containing artificial LNA-amide linkages with improved gymnotic cell uptake, RNA affinity, stability and potency. To construct such oligonucleotides, five LNA-amide monomers (A, T, C, 5mC and G), where the 3′-OH is replaced by an ethanoic acid group, are synthesised in good yield and used in solid-phase oligonucleotide synthesis to form amide linkages with high efficiency. The artificial backbone causes minimal structural deviation to the DNA:RNA duplex. These studies indicate that splice-switching oligonucleotides containing LNA-amide linkages and phosphorothioates display improved activity relative to oligonucleotides lacking amides, highlighting the therapeutic potential of this technology.

Oligonucleotides targeting mRNA are promising therapeutic agents but suffer from poor bioavailability. Here, the authors develop reduced-charge oligonucleotides with artificial LNA-amide linkages with improved cell uptake and minimal structural deviation to the DNA:RNA duplex.

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.

Lessons Learned and Yet-to-Be Learned on the Importance of RNA Structure in SARS-CoV-2 Replication

SARS-CoV-2, the etiological agent responsible for the COVID-19 pandemic, is a member of the virus family Coronaviridae, known for relatively extensive (~30-kb) RNA genomes that not only encode for numerous proteins but are also capable of forming elaborate structures. As highlighted in this review, these structures perform critical functions in various steps of the viral life cycle, ultimately impacting pathogenesis and transmissibility. We examine these elements in the context of coronavirus evolutionary history and future directions for curbing the spread of SARS-CoV-2 and other potential human coronaviruses. While we focus on structures supported by a variety of biochemical, biophysical, and/or computational methods, we also touch here on recent evidence for novel structures in both protein-coding and noncoding regions of the genome, including an assessment of the potential role for RNA structure in the controversial finding of SARS-CoV-2 integration in “long COVID” patients. This review aims to serve as a consolidation of previous works on coronavirus and more recent investigation of SARS-CoV-2, emphasizing the need for improved understanding of the role of RNA structure in the evolution and adaptation of these human viruses.

RNAspider: a webserver to analyze entanglements in RNA 3D structures

Advances in experimental and computational techniques enable the exploration of large and complex RNA 3D structures. These, in turn, reveal previously unstudied properties and motifs not characteristic for small molecules with simple architectures. Examples include entanglements of structural elements in RNA molecules and knot-like folds discovered, among others, in the genomes of RNA viruses. Recently, we presented the first classification of entanglements, determined by their topology and the type of entangled structural elements. Here, we introduce RNAspider - a web server to automatically identify, classify, and visualize primary and higher-order entanglements in RNA tertiary structures. The program applies to evaluate RNA 3D models obtained experimentally or by computational prediction. It supports the analysis of uncommon topologies in the pseudoknotted RNA structures. RNAspider is implemented as a publicly available tool with a user-friendly interface and can be freely accessed at https://rnaspider.cs.put.poznan.pl/.

The dynamic, motile and deformative properties of RNA nanoparticles facilitate the third milestone of drug development

Besides mRNA, rRNA, and tRNA, cells contain many other noncoding RNA that display critical roles in the regulation of cellular functions. Human genome sequencing revealed that the majority of non-protein-coding DNA actually codes for non-coding RNAs. The dynamic nature of RNA results in its motile and deformative behavior. These conformational transitions such as the change of base-pairing, breathing within complemented strands, and pseudoknot formation at the 2D level as well as the induced-fit and conformational capture at the 3D level are important for their biological functions including regulation, translation, and catalysis. The dynamic, motile and catalytic activity has led to a belief that RNA is the origin of life. We have recently reported that the deformative property of RNA nanoparticles enhances their penetration through the leaky blood vessel of cancers which leads to highly efficient tumor accumulation. This special deformative property also enables RNA nanoparticles to pass the glomerulus, overcoming the filtration size limit, resulting in fast renal excretion and rapid body clearance, thus low or no toxicity. The biodistribution of RNA nanoparticles can be further improved by the incorporation of ligands for cancer targeting. In addition to the favorable biodistribution profiles, RNA nanoparticles possess other properties including self-assembly, negative charge, programmability, and multivalency; making it a great material for pharmaceutical applications. The intrinsic negative charge of RNA nanoparticles decreases the toxicity of drugs by preventing nonspecific binding to the negative charged cell membrane and enhancing the solubility of hydrophobic drugs. The polyvalent property of RNA nanoparticles allows the multi-functionalization which can apply to overcome drug resistance. This review focuses on the summary of these unique properties of RNA nanoparticles, which describes the mechanism of RNA dynamic, motile and deformative properties, and elucidates and prepares to welcome the RNA therapeutics as the third milestone in pharmaceutical drug development.

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.

Dual-band fluorescence detection of double-stranded DNA with QDs-Mn2+-pefloxacin

By integrating the fluorescence of quantum dots (QDs) and Mn²⁺-pefloxacin mesylate (Mn²⁺-pefloxacin), a new type of dual-band fluorescence biosensor for high-efficiency and sensitive determination of double-stranded DNA (dsDNA) is developed. The biosensor is based on the fluorescence "OFF-ON" mode of both QDs and QDs-Mn²⁺-pefloxacin. The Mn²⁺-pefloxacin complex can quench the QDs fluorescence via photoinduced electron transfer (PET), and its fluorescence is also quenched. Due to the specificity and strong binding affinity of dsDNA for the Mn²⁺-pefloxacin complex, it can break the low fluorescent QDs-Mn²⁺-pefloxacin and restore the fluorescence of QDs and Mn²⁺-pefloxacin complex in their respective bands. Therefore, the dual-band fluorescence quantitative detection of dsDNA by QDs-Mn²⁺-pefloxacin can be achieved, while bovine serum albumin, single-stranded DNA, and bio-related ions do not yield similar results. Furthermore, the possible reaction mechanisms are systematically discussed. The detection limits (3δ/K) of herring sperm (hs) DNA in the fluorescence recovery bands of QDs and Mn²⁺-pefloxacin complex are 0.0142 and 0.0465 μg/mL, respectively. The developed biosensor was used for dsDNA detection in synthetic samples, and desirable results are obtained.

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

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

Affinity-Based Profiling of the Flavin Mononucleotide Riboswitch

Riboswitches are structural RNA elements that control gene expression. These naturally occurring RNA sensors are of continued interest as antibiotic targets, molecular sensors, and functional elements of synthetic circuits. Here, we describe affinity-based profiling of the flavin mononucleotide (FMN) riboswitch to characterize ligand binding and structural folding. We designed and synthesized photoreactive ligands and used them for photoaffinity labeling. We showed selective labeling of the FMN riboswitch and used this covalent interaction to quantitatively measure ligand binding, which we demonstrate with the naturally occurring antibiotic roseoflavin. We measured conditional riboswitch folding as a function of temperature and cation concentration. Furthermore, combining photoaffinity labeling with reverse transcription revealed ligand binding sites within the aptamer domain with single-nucleotide resolution. The photoaffinity probe was applied to cellular extracts of Bacillus subtilis to demonstrate conditional folding of the endogenous low-abundant ribD FMN riboswitch in biologically derived samples using quantitative PCR. Lastly, binding of the riboswitch-targeting antibiotic roseoflavin to the FMN riboswitch was measured in live bacteria using the photoaffinity probe.

Pharmacophore-Based Discovery of Viral RNA Conformational Modulators

New RNA-binding small-molecule scaffolds are needed to unleash the pharmacological potential of RNA targets. Here we have applied a pharmacophore-based virtual screening approach, seldom used in the RNA recognition field, to identify novel conformational inhibitors of the hepatitis C virus internal ribosome entry site. The conformational effect of the screening hits was assessed with a fluorescence resonance energy transfer assay, and the affinity, specificity, and binding site of the ligands were determined using a combination of fluorescence intensity and NMR spectroscopy experiments. The results indicate that this strategy can be successfully applied to discover RNA conformational inhibitors bearing substantially less positive charge than the reference ligands. This methodology can potentially be accommodated to other RNA motifs of pharmacological interest, facilitating the discovery of novel RNA-targeted molecules.

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

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

Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.

SHAPE-enabled fragment-based ligand discovery for RNA

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

Translating from proteins to ribonucleic acids for ligand-binding site detection

Identifying druggable ligand-binding sites on the surface of the macromolecular targets is an important process in structure-based drug discovery. Deep-learning models have been shown to successfully predict ligand-binding sites of proteins. As a step toward predicting binding sites in RNA and RNA-protein complexes, we employ three-dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure-related bias in training data, and investigate the influence of protein-based neural network pre-training before fine-tuning on RNA structures. Models that were pre-trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71% of the known RNA binding sites were correctly located within 4 Å of their true centres with a structural overlap of at least 25%.

RNA-Binding Macrocyclic Peptides

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

Rational guide RNA engineering for small-molecule control of CRISPR/Cas9 and gene editing

It is important to control CRISPR/Cas9 when sufficient editing is obtained. In the current study, rational engineering of guide RNAs (gRNAs) is performed to develop small-molecule-responsive CRISPR/Cas9. For our purpose, the sequence of gRNAs are modified to introduce ligand binding sites based on the rational design of ligand-RNA pairs. Using short target sequences, we demonstrate that the engineered RNA provides an excellent scaffold for binding small molecule ligands. Although the 'stem-loop 1' variants of gRNA induced variable cleavage activity for different target sequences, all 'stem-loop 3' variants are well tolerated for CRISPR/Cas9. We further demonstrate that this specific ligand-RNA interaction can be utilized for functional control of CRISPR/Cas9 in vitro and in human cells. Moreover, chemogenetic control of gene editing in human cells transfected with all-in-one plasmids encoding Cas9 and designer gRNAs is demonstrated. The strategy may become a general approach for generating switchable RNA or DNA for controlling other biological processes.

Sub-3-Å cryo-EM structure of RNA enabled by engineered homomeric self-assembly

High-resolution structural studies are essential for understanding the folding and function of diverse RNAs. Herein, we present a nanoarchitectural engineering strategy for efficient structural determination of RNA-only structures using single-particle cryogenic electron microscopy (cryo-EM). This strategy-ROCK (RNA oligomerization-enabled cryo-EM via installing kissing loops)-involves installing kissing-loop sequences onto the functionally nonessential stems of RNAs for homomeric self-assembly into closed rings with multiplied molecular weights and mitigated structural flexibility. ROCK enables cryo-EM reconstruction of the Tetrahymena group I intron at 2.98-Å resolution overall (2.85 Å for the core), allowing de novo model building of the complete RNA, including the previously unknown peripheral domains. ROCK is further applied to two smaller RNAs-the Azoarcus group I intron and the FMN riboswitch, revealing the conformational change of the former and the bound ligand in the latter. ROCK holds promise to greatly facilitate the use of cryo-EM in RNA structural studies.

RNA-ligand molecular docking: advances and challenges

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

Targeting Ribosome Biogenesis in Cancer: Lessons Learned and Way Forward

Simple Summary

Cells need to produce ribosomes to sustain continuous proliferation and expand in numbers, a feature that is even more prominent in uncontrollably proliferating cancer cells. Certain cancer cell types are expected to depend more on ribosome biogenesis based on their genetic background, and this potential vulnerability can be exploited in designing effective, targeted cancer therapies. This review provides information on anti-cancer molecules that target the ribosome biogenesis machinery and indicates avenues for future research.

Abstract

Rapid growth and unrestrained proliferation is a hallmark of many cancers. To accomplish this, cancer cells re-wire and increase their biosynthetic and metabolic activities, including ribosome biogenesis (RiBi), a complex, highly energy-consuming process. Several chemotherapeutic agents used in the clinic impair this process by interfering with the transcription of ribosomal RNA (rRNA) in the nucleolus through the blockade of RNA polymerase I or by limiting the nucleotide building blocks of RNA, thereby ultimately preventing the synthesis of new ribosomes. Perturbations in RiBi activate nucleolar stress response pathways, including those controlled by p53. While compounds such as actinomycin D and oxaliplatin effectively disrupt RiBi, there is an ongoing effort to improve the specificity further and find new potent RiBi-targeting compounds with improved pharmacological characteristics. A few recently identified inhibitors have also become popular as research tools, facilitating our advances in understanding RiBi. Here we provide a comprehensive overview of the various compounds targeting RiBi, their mechanism of action, and potential use in cancer therapy. We discuss screening strategies, drug repurposing, and common problems with compound specificity and mechanisms of action. Finally, emerging paths to discovery and avenues for the development of potential biomarkers predictive of therapeutic outcomes across cancer subtypes are also presented.