Small molecules targeting viral RNA

Identification of Small-Molecule Inhibitors for Enterovirus A71 IRES by Structure-Based Virtual Screening

Structured RNAs play a crucial role in regulating gene expression, which includes both protein synthesis and RNA processing. Dysregulation of these processes is associated with various conditions, including viral and bacterial infections, as well as cancer. The unique tertiary structures of structured RNAs provide an opportunity for small molecules to directly modulate such processes, making them promising targets for drug discovery. Although small-molecule inhibitors targeting RNA have shown early success, in silico strategies like structure-based virtual screening remain underutilized for RNA-targeted drug discovery. In this study, we developed a virtual screening scheme targeting the structural ensemble of EV-A71 IRES SL II, a noncoding viral RNA element essential for viral replication. We subsequently optimized the experimentally validated hit compound IRE-03 from virtual screening through an "analog-by-catalog" search. This led to the identification of a more potent IRES inhibitor, IRE-03-3, validated through biochemical and functional assays with an EC50 value of 11.96 μM against viral proliferation. Our findings demonstrate that structure-based virtual screening can be effectively applied to RNA targets, providing exciting new opportunities for future antiviral drug discovery.

Targeting Two-Tetrad RNA G-Quadruplex in the SARS-CoV-2 RNA Genome Using Tetraphenylethene Derivatives for Antiviral Therapy

Targeting the specific RNA conformations that are crucial for SARS-CoV-2 replication is a viable antiviral approach. The SARS-CoV-2 genome contains GG repeats capable of forming unstable two-tetrad G-quadruplex (GQ) structures, which exist as a mix of conformations, including hairpin (Hp), intra-, and intermolecular GQs. RGQ-1, originating from the nucleocapsid gene's ORF, adopts a dynamic equilibrium of conformations, including intramolecular hairpin and G-quadruplex (Hp-GQ) structures, as confirmed by CD analysis. In this study, tetraphenylethene (TPE) derivatives were developed to target the Hp-GQ conformational equilibrium of RGQ-1. EMSA, fluorescence spectroscopy, and ITC assays confirmed that two TPE derivatives, TPE-MePy and TPE-Allyl Py, bind to RGQ-1. CD thermal melting experiments indicate that RGQ-1 is stabilized by 8.56 and 12.54 °C in the presence of TPE-MePy and TPE-Allyl Py, respectively. Additionally, luciferase assays demonstrated that TPE derivatives suppressed luciferase activity by 2.2-fold and 3.6-fold, respectively, shifting the HpGQ equilibrium toward the GQ conformation, as suggested by CD spectroscopy. Treatment of SARS-CoV-2-infected A549 cells with TPE derivatives reduced the levels of viral RNA, spikes, and nucleocapsid proteins. To explore their antiviral mechanism, preinfection and postinfection treatments were tested, revealing that the TPE derivatives specifically suppressed the postentry stages of viral replication without affecting viral entry. These findings highlight the therapeutic potential of TPE derivatives in inhibiting key gene expressions critical for SARS-CoV-2 replication.

Alternative Approach to Sequence-Specific Recognition of DNA: Cooperative Stacking of Dication Dimers─Sensitivity to Compound Curvature, Aromatic Structure, and DNA Sequence

With the growing number and diversity of known genome sequences, there is an increasing opportunity to regulate gene expression through synthetic, cell-permeable small molecules. Enhancing the DNA sequence recognition abilities of minor groove compounds has the potential to broaden their therapeutic applications with significant implications for areas such as modulating transcription factor activity. While various classes of minor groove binding agents can selectively identify pure AT and mixed AT and GC base pair(s) containing sequences, there remains a lack of compounds capable of distinguishing between different AT sequences. In this work, we report on the design compounds that exhibit selective binding to -TTAA- or -TATA- containing DNA minor groove sequences compared with other AT ones. Several studies have shown that the -AATT- and -TTAA- sequences have distinct physical and interaction properties, especially in terms of their different requirements for recognition in the minor groove. Achieving strong, selective minor groove binding at -TTAA- sequences has been challenging, but DB1003, a benzimidazole-furan-furan diamidine, has demonstrated cooperative dimeric binding activity at -TTAA-. It has significantly less binding preference for AATT. To better understand and modify the selectivity, we synthesized a set of rationally designed analogs of DB1003 by altering the position of the five-membered heterocyclic structure. Binding affinities and stoichiometries obtained from biosensor-surface plasmon resonance experiments show that DB1992, a benzimidazolefuran-thiophene diamidine, binds strongly to -TTAA- as a positive cooperative dimer with high cooperativity. The high-resolution crystal structure of the TTAA-DNA-DB1992 complex reveals that DB1992 binds as an antiparallel π-stacked dimer with numerous diverse contacts to the DNA minor groove. This distinctive binding arrangement and the properties of diamidines at the -TTAA- minor groove demonstrate that benzimidazole-furan-thiophene is a unique DNA binding pharmacophore. Competition mass spectroscopy and circular dichroism studies confirmed the binding stoichiometry and selectivity preference of the compounds for the -TTAA- sequence.

Novel Quinazoline Derivatives Inhibit Splicing of Fungal Group II Introns

We report the discovery of small molecules that target the RNA tertiary structure of self-splicing group II introns and display potent antifungal activity against yeasts, including the major public health threat Candida parapsilosis. High-throughput screening efforts against a yeast group II intron resulted in an inhibitor class which was then synthetically optimized for enhanced inhibitory activity and antifungal efficacy. The most highly refined compounds in this series display strong, gene-specific antifungal activity against C. parapsilosis. This work demonstrates the utility of combining advanced RNA screening methodologies with medicinal chemistry pipelines to identify high-affinity ligands targeting RNA tertiary structures with important roles in human health and disease.

Design and Evaluation of Azaspirocycles as RNA binders

This study presents efficient synthetic pathways for preparing novel azaspirocycles. These methodologies involve functionalizing key bicyclic hydrazines with a substituent on one of their bridgehead carbon atoms. The desired spirocyclic cores were successfully obtained through double reductive amination reactions, intramolecular cyclizations, and cleavages of the N-N bond. The isolated molecules possess unique three-dimensional structures, suggesting potential applications in medicinal chemistry and drug discovery. With the growing interest in targeting nucleic acids as a complementary approach to protein-targeting strategies for developing novel active compounds, we investigated the potential of the synthesized azaspirocycles as RNA binders. As a proof of concept, we highlight the promising activity of some compounds as strong binders of HIV-1 TAR RNA and inhibitors of Tat/TAR interactions.

Design of triplex-forming peptide nucleic acid-based fluorescent probes for forced intercalation sensing of double-stranded RNA structures

The diverse functional roles of RNA within cells have led to a growing interest in developing RNA-binding fluorescent probes to investigate RNA functions. In particular, the probes for double-stranded RNA (dsRNA) structures are of significant value given the importance of the secondary and tertiary RNA structures on their biologic functions. This review highlights our recent efforts on the development of triplex-forming peptide nucleic acid (TFP)-based probes for fluorescence sensing of dsRNA structures. We demonstrated that the forced intercalation of asymmetric cyanine dyes integrated as base surrogate within the probes was useful for achieving significant light-up response toward target dsRNAs. We also showed that the TFP probes conjugated with small RNA-binding molecules facilitated the fluorescence sensing of biologic relevant dsRNAs containing unpaired nucleobases. The binding and fluorescence signaling functions of such probes were discussed, emphasizing their potential as analytical tools for studying dsRNA structures.

Harnessing Computational Approaches for RNA-Targeted Drug Discovery

RNA molecules have emerged as promising therapeutic targets due to their diverse functional and regulatory roles within cells. Computational modeling in RNA-targeted drug discovery presents a significant opportunity to expedite the discovery of novel small molecule compounds. However, this field encounters unique challenges compared to protein-targeted drug design, primarily due to limited experimental data availability and current models' inability to adequately address RNA's conformational flexibility during ligand recognition. Despite these challenges, several studies have successfully identified active RNA-targeting compounds using structure-based approaches or quantitative structure-activity relationship (QSAR) models. This review offers an overview of recent advancements in modeling RNA-small molecule interactions, emphasizing practical applications of computational methods in RNA-targeted drug discovery. Additionally, we survey existing databases that catalog nucleic acid-small molecule interactions. As interest in RNA-small molecule interactions grows and curated databases expand, the field anticipates rapid development. Novel computational models are poised to enhance the identification of potent and selective small-molecule modulators for therapeutic needs.

Structure of Essential RNA Regulatory Elements in the West Nile Virus 3'-Terminal Stem Loop

Flaviviruses, such as West Nile and Dengue Virus, pose a significant and growing threat to global health. Central to the flavivirus life cycle are highly structured 5'- and 3'-untranslated regions (UTRs), which harbor conserved cis-acting RNA elements critical for viral replication and host adaptation. Despite their essential roles, detailed molecular insights into these RNA elements have been limited. By employing nuclear magnetic resonance (NMR) spectroscopy in conjunction with SAXS experiments, we determined the three-dimensional structure of the West Nile Virus (WNV) 3'-terminal stem-loop core, a highly conserved element critical for viral genome cyclization and replication. Single nucleotide mutations at several sites within this RNA abolish the ability of the virus to replicate. These critical sites are located within a short 18-nucleotide hairpin stem, a substructure notable for its conformational flexibility, while the adjoining main stem-loop adopts a well-defined extended helix interrupted by three non-Watson-Crick pairs. This study enhances our understanding of several metastable RNA structures that play key roles in regulating the flavivirus lifecycle, and thereby also opens up potential new avenues for the development of antivirals targeting these conserved RNA structures. In particular, the structure we observe suggests that the plastic junction between the small hairpin and the tail of the longer stem-loop could provide a binding pocket for small molecules, for example potentially stabilizing the RNA in a conformation which hinders the conformational rearrangements critical for viral replication.

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.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Adjusting the Energy Profile for CH-O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 μs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

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

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

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

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

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.

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

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

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.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

Highly Potent Antisense Oligonucleotides Locked Nucleic Acid Gapmers Targeting the SARS-CoV-2 RNA Genome

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused the current worldwide pandemic and the associated coronavirus disease 2019 with potentially lethal outcome. Although effective vaccines strongly contributed to reduce disease severity, establishing a toolbox to control current and newly emerging coronaviruses of epidemic concern requires the development of novel therapeutic compounds, to treat severely infected individuals and to prevent virus transmission. Here we present a therapeutic strategy targeting the SARS-CoV-2 RNA genome using antisense oligonucleotides (ASOs). We demonstrate that selected locked nucleic acid gapmers have the potency to reduce the in vitro intracellular viral load by up to 96%. Our promising results strongly support the case for further development of our preselected ASOs as therapeutic or prophylactic antiviral agents.

Probing Bioactive Chemical Space to Discover RNA-Targeted Small Molecules

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

Native Top-Down Mass Spectrometry Uncovers Two Distinct Binding Motifs of a Functional Neomycin-Sensing Riboswitch Aptamer

Understanding how ligands bind to ribonucleic acids (RNA) is important for understanding RNA recognition in biological processes and drug development. Here, we have studied neomycin B binding to neomycin-sensing riboswitch aptamer constructs by native top-down mass spectrometry (MS) using electrospray ionization (ESI) and collisionally activated dissociation (CAD). Our MS data for a 27 nt aptamer construct reveal the binding site and ligand interactions, in excellent agreement with the structure derived from nuclear magnetic resonance (NMR) studies. Strikingly, for an extended 40 nt aptamer construct, which represents the sequence with the highest regulatory factor for riboswitch function, we identified two binding motifs for neomycin B binding, one corresponding to the bulge-loop motif of the 27 nt construct and the other one in the minor groove of the lower stem, which according to the MS data are equally populated. By replacing a noncanonical with a canonical base pair in the lower stem of the 40 nt aptamer, we can reduce binding to the minor groove motif from ∼50 to ∼30%. Conversely, the introduction of a CUG/CUG motif in the lower stem shifts the binding equilibrium in favor of minor groove binding. The MS data reveal site-specific and stoichiometry-resolved information on aminoglycoside binding to RNA that is not directly accessible by other methods and underscore the role of noncanonical base pairs in RNA recognition by aminoglycosides.

The multifaceted roles of mass spectrometric analysis in nucleic acids drug discovery and development

The deceptively simple concepts of mass determination and fragment analysis are the basis for the application of mass spectrometry (MS) to a boundless range of analytes, including fundamental components and polymeric forms of nucleic acids (NAs). This platform affords the intrinsic ability to observe first-hand the effects of NA-active drugs on the chemical structure, composition, and conformation of their targets, which might affect their ability to interact with cognate NAs, proteins, and other biomolecules present in a natural environment. The possibility of interfacing with high-performance separation techniques represents a multiplying factor that extends these capabilities to cover complex sample mixtures obtained from organisms that were exposed to NA-active drugs. This report provides a brief overview of these capabilities in the context of the analysis of the products of NA-drug activity and NA therapeutics. The selected examples offer proof-of-principle of the applicability of this platform to all phases of the journey undertaken by any successful NA drug from laboratory to bedside, and provide the rationale for its rapid expansion outside traditional laboratory settings in support to ever growing manufacturing operations.

Conformational dynamics of RNA G4C2 and G2C4 repeat expansions causing ALS/FTD using NMR and molecular dynamics studies

G4C2 and G2C4 repeat expansions in chromosome 9 open reading frame 72 (C9orf72) are the most common cause of genetically defined amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), or c9ALS/FTD. The gene is bidirectionally transcribed, producing G4C2 repeats [r(G4C2)exp] and G2C4 repeats [r(G2C4)exp]. The c9ALS/FTD repeat expansions are highly structured, and structural studies showed that r(G4C2)exp predominantly folds into a hairpin with a periodic array of 1 × 1 G/G internal loops and a G-quadruplex. A small molecule probe revealed that r(G4C2)exp also adopts a hairpin structure with 2 × 2 GG/GG internal loops. We studied the conformational dynamics adopted by 2 × 2 GG/GG loops using temperature replica exchange molecular dynamics (T-REMD) and further characterized the structure and underlying dynamics using traditional 2D NMR techniques. These studies showed that the loop's closing base pairs influence both structure and dynamics, particularly the configuration adopted around the glycosidic bond. Interestingly, r(G2C4) repeats, which fold into an array of 2 × 2 CC/CC internal loops, are not as dynamic. Collectively, these studies emphasize the unique sensitivity of r(G4C2)exp to small changes in stacking interactions, which is not observed in r(G2C4)exp, providing important considerations for further principles in structure-based drug design.

Real-time label-free detection of dynamic aptamer-small molecule interactions using a nanopore nucleic acid conformational sensor

Nucleic acids can undergo conformational changes upon binding small molecules. These conformational changes can be exploited to develop new therapeutic strategies through control of gene expression or triggering of cellular responses and can also be used to develop sensors for small molecules such as neurotransmitters. Many analytical approaches can detect dynamic conformational change of nucleic acids, but they need labeling, are expensive, and have limited time resolution. The nanopore approach can provide a conformational snapshot for each nucleic acid molecule detected, but has not been reported to detect dynamic nucleic acid conformational change in response to small -molecule binding. Here we demonstrate a modular, label-free, nucleic acid-docked nanopore capable of revealing time-resolved, small molecule-induced, single nucleic acid molecule conformational transitions with millisecond resolution. By using the dopamine-, serotonin-, and theophylline-binding aptamers as testbeds, we found that these nucleic acids scaffolds can be noncovalently docked inside the MspA protein pore by a cluster of site-specific charged residues. This docking mechanism enables the ion current through the pore to characteristically vary as the aptamer undergoes conformational changes, resulting in a sequence of current fluctuations that report binding and release of single ligand molecules from the aptamer. This nanopore tool can quantify specific ligands such as neurotransmitters, elucidate nucleic acid-ligand interactions, and pinpoint the nucleic acid motifs for ligand binding, showing the potential for small molecule biosensing, drug discovery assayed via RNA and DNA conformational changes, and the design of artificial riboswitch effectors in synthetic biology.

Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions

The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.

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.

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.

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.

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.

Fluorescence Sensing of the Panhandle Structure of the Influenza A Virus RNA Promoter by Thiazole Orange Base Surrogate-Carrying Peptide Nucleic Acid Conjugated with Small Molecule

We have developed a new class of triplex-forming peptide nucleic acid (PNA)-based fluorogenic probes for sensing of the panhandle structure of the influenza A virus (IAV) RNA promoter region. Here, a small molecule (DPQ) capable of selectively binding to the internal loop structure was conjugated with triplex-forming forced intercalation of the thiazole orange (tFIT) probe with natural PNA nucleobases. The resulting conjugate, tFIT-DPQ, showed a significant light-up response (83-fold) upon strong (K_d = 107 nM) and structure-selective binding to the IAV RNA promoter region under physiological conditions (pH 7.0, 100 mM NaCl). We demonstrated the conjugation of these two units through the suitable spacer was key to show useful binding and fluorogenic signaling functions. tFIT-DPQ facilitated the sensitive and selective detection of IAV RNA based on its binding to the promoter region. Furthermore, we found that tFIT-DPQ could work as a sensitive indicator for screening of test compounds targeting the IAV RNA promoter region in the fluorescence indicator displacement assay.

RNA-Binding Macrocyclic Peptides

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

RNA-ligand molecular docking: advances and challenges

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

Recent advancement in small molecules as HCV inhibitors

Hepatitis C virus (HCV) has caused a considerable threat to human health. To date, no treatments are without side effects. The proteins and RNA associated with HCV have specific functions during the viral life cycle. The vulnerabilities to virus are associated with those proteins or RNA. Thus, targeting these proteins and RNA is an efficient strategy to develop anti-HCV therapeutics. The treatment for HCV-infected patients has been greatly improved after the approval of direct-acting antivirals (DAAs). However, the cost of DAAs is unusually high, which adds to the economic burden on patients with chronic liver diseases. So far, many efforts have been devoted to the development of small molecules as novel HCV inhibitors. Investigations on the inhibitory activities of these small molecules have involved the target identification and the mechanism of action. In this mini-review, these small molecules divided into four kinds were elaborated, which focused on their targets and structural features. Furthermore, we raised the current challenges and promising prospects. This mini-review may facilitate the development of small molecules with improved activities targeting HCV based on the chemical scaffolds of HCV inhibitors.

Subsite Ligand Recognition and Cooperativity in the TPP Riboswitch: Implications for Fragment-Linking in RNA Ligand Discovery

RNA molecules can show high levels of cooperativity in their global folding and interactions with divalent ions. However, cooperativity at individual ligand-RNA interaction sites remains poorly understood. Here, we investigated the binding of thiamine and methylene diphosphonic acid (MDP, a soluble structural analogue of pyrophosphate) to the thiamine pyrophosphate riboswitch. These ligands each bind weakly at proximal subsites, with 10 μM and 1 mM affinities, respectively. The affinity of MDP moderately improves when thiamine or thiamine-like fragments are pre-bound to the RNA. Covalent linking of thiamine and MDP substantially increases riboswitch binding to a notable high affinity of 20 nM. Crystal structures and single-molecule correlated chemical probing revealed favorable induced fit effects upon binding of individual ligands and, unexpectedly, a substantial thermodynamically unfavorable RNA structural rearrangement upon binding of the linked thiamine-MDP ligand. Thus, linking of two ligands of modest affinity, accompanied by an unfavorable structural rearrangement, still yields a potent linked RNA-binding compound. Since complex ligands often bind riboswitches and other RNAs at proximal subsites, principles derived from this work inform and support fragment-linking strategies for identifying small molecules that interact with RNA specifically and with high affinity.

Overview of Methods for Large-Scale RNA Synthesis

In recent years, it has become clear that RNA molecules are involved in almost all vital cellular processes and pathogenesis of human disorders. The functional diversity of RNA comes from its structural richness. Although composed of only four nucleotides, RNA molecules present a plethora of secondary and tertiary structures critical for intra and intermolecular contacts with other RNAs and ligands (proteins, small metabolites, etc.). In order to fully understand RNA function it is necessary to define its spatial structure. Crystallography, nuclear magnetic resonance and cryogenic electron microscopy have demonstrated considerable success in determining the structures of biologically important RNA molecules. However, these powerful methods require large amounts of sample. Despite their limitations, chemical synthesis and in vitro transcription are usually employed to obtain milligram quantities of RNA for structural studies, delivering simple and effective methods for large-scale production of homogenous samples. The aim of this paper is to provide an overview of methods for large-scale RNA synthesis with emphasis on chemical synthesis and in vitro transcription. We also present our own results of testing the efficiency of these approaches in order to adapt the material acquisition strategy depending on the desired RNA construct.

Potential for the Development of a New Generation of Aminoglycoside Antibiotics

The present review summarizes recent publications devoted to aminoglycosides that study the main types of resistance to antibiotics of this class and the main directions of chemical modification aimed at overcoming the resistance or changing the spectrum of biological activity. Conjugates of aminoglycosides with various pharmacophores including amino acids, peptides, peptide nucleic acids, nucleic bases, and several other biologically active molecules and modifications resulting in other types of biological activity of this class of antibiotics are described. It is concluded that a promising research direction aimed at increasing the activity of antibiotics against resistant strains is the search for selective inhibitors of aminoglycoside-modifying enzymes. This would allow renewal of the use of antibiotics already meeting widespread resistance and would increase the potential of a new generation of antibiotics.

RLDOCK method for predicting RNA-small molecule binding modes

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.

Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures

The SARS-CoV-2 pandemic, and the likelihood of future coronavirus pandemics, emphasized the urgent need for development of novel antivirals. Small-molecule chemical probes offer both to reveal aspects of virus replication and to serve as leads for antiviral therapeutic development. Here, we report on the identification of amiloride-based small molecules that potently inhibit OC43 and SARS-CoV-2 replication through targeting of conserved structured elements within the viral 5′-end. Nuclear magnetic resonance–based structural studies revealed specific amiloride interactions with stem loops containing bulge like structures and were predicted to be strongly bound by the lead amilorides in retrospective docking studies. Amilorides represent the first antiviral small molecules that target RNA structures within the 5′ untranslated regions and proximal region of the CoV genomes. These molecules will serve as chemical probes to further understand CoV RNA biology and can pave the way for the development of specific CoV RNA–targeted antivirals.

Mass Spectrometry of Nucleic Acid Noncovalent Complexes

Nucleic acids have been among the first targets for antitumor drugs and antibiotics. With the unveiling of new biological roles in regulation of gene expression, specific DNA and RNA structures have become very attractive targets, especially when the corresponding proteins are undruggable. Biophysical assays to assess target structure as well as ligand binding stoichiometry, affinity, specificity, and binding modes are part of the drug development process. Mass spectrometry offers unique advantages as a biophysical method owing to its ability to distinguish each stoichiometry present in a mixture. In addition, advanced mass spectrometry approaches (reactive probing, fragmentation techniques, ion mobility spectrometry, ion spectroscopy) provide more detailed information on the complexes. Here, we review the fundamentals of mass spectrometry and all its particularities when studying noncovalent nucleic acid structures, and then review what has been learned thanks to mass spectrometry on nucleic acid structures, self-assemblies (e.g., duplexes or G-quadruplexes), and their complexes with ligands.

virusMED: an atlas of hotspots of viral proteins

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Exploring the Druggability of Conserved RNA Regulatory Elements in the SARS-CoV-2 Genome

SARS-CoV-2 contains a positive single-stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS-CoV and SARS-CoV-2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex-vivo structural probing experiments. These elements contain non-base-paired regions that potentially harbor ligand-binding pockets. Here, we performed an NMR-based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1 H-based 1D NMR binding assays. The screening identified common as well as RNA-element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS-CoV-2.

Supramolecular Cylinders Target Bulge Structures in the 5' UTR of the RNA Genome of SARS-CoV-2 and Inhibit Viral Replication

The untranslated regions (UTRs) of viral genomes contain a variety of conserved yet dynamic structures crucial for viral replication, providing drug targets for the development of broad spectrum anti-virals. We combine in vitro RNA analysis with molecular dynamics simulations to build the first 3D models of the structure and dynamics of key regions of the 5' UTR of the SARS-CoV-2 genome. Furthermore, we determine the binding of metallo-supramolecular helicates (cylinders) to this RNA structure. These nano-size agents are uniquely able to thread through RNA junctions and we identify their binding to a 3-base bulge and the central cross 4-way junction located in stem loop 5. Finally, we show these RNA-binding cylinders suppress SARS-CoV-2 replication, highlighting their potential as novel anti-viral agents.

Supramolecular Cylinders Target Bulge Structures in the 5' UTR of the RNA Genome of SARS-CoV-2 and Inhibit Viral Replication*

The untranslated regions (UTRs) of viral genomes contain a variety of conserved yet dynamic structures crucial for viral replication, providing drug targets for the development of broad spectrum anti-virals. We combine in vitro RNA analysis with molecular dynamics simulations to build the first 3D models of the structure and dynamics of key regions of the 5' UTR of the SARS-CoV-2 genome. Furthermore, we determine the binding of metallo-supramolecular helicates (cylinders) to this RNA structure. These nano-size agents are uniquely able to thread through RNA junctions and we identify their binding to a 3-base bulge and the central cross 4-way junction located in stem loop 5. Finally, we show these RNA-binding cylinders suppress SARS-CoV-2 replication, highlighting their potential as novel anti-viral agents.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

Bis(phenylethynyl)arene Linkers in Tetracationic Bis-triarylborane Chromophores Control Fluorimetric and Raman Sensing of Various DNAs and RNAs

We report four new luminescent tetracationic bis-triarylborane DNA and RNA sensors that show high binding affinities, in several cases even in the nanomolar range. Three of the compounds contain substituted, highly emissive and structurally flexible bis(2,6-dimethylphenyl-4-ethynyl)arene linkers (3: arene=5,5'-2,2'-bithiophene; 4: arene=1,4-benzene; 5: arene=9,10-anthracene) between the two boryl moieties and serve as efficient dual Raman and fluorescence chromophores. The shorter analogue 6 employs 9,10-anthracene as the linker and demonstrates the importance of an adequate linker length with a certain level of flexibility by exhibiting generally lower binding affinities than 3-5. Pronounced aggregation-deaggregation processes are observed in fluorimetric titration experiments with DNA for compounds 3 and 5. Molecular modelling of complexes of 5 with AT-DNA, suggest the minor groove as the dominant binding site for monomeric 5, but demonstrate that dimers of 5 can also be accommodated. Strong SERS responses for 3-5 versus a very weak response for 6, particularly the strong signals from anthracene itself observed for 5 but not for 6, demonstrate the importance of triple bonds for strong Raman activity in molecules of this compound class. The energy of the characteristic stretching vibration of the C≡C bonds is significantly dependent on the aromatic moiety between the triple bonds. The insertion of aromatic moieties between two C≡C bonds thus offers an alternative design for dual Raman and fluorescence chromophores, applicable in multiplex biological Raman imaging.

Targeting RNA with small molecules: from fundamental principles towards the clinic

Recent advances in our understanding of RNA biology have uncovered crucial roles for RNA in multiple disease states, ranging from viral and bacterial infections to cancer and neurological disorders. As a result, multiple laboratories have become interested in developing drug-like small molecules to target RNA. However, this development comes with multiple unique challenges. For example, RNA is inherently dynamic and has limited chemical diversity. In addition, promiscuous RNA-binding ligands are often identified during screening campaigns. This Tutorial Review overviews important considerations and advancements for generating RNA-targeted small molecules, ranging from fundamental chemistry to promising small molecule examples with demonstrated clinical efficacy. Specifically, we begin by exploring RNA functional classes, structural hierarchy, and dynamics. We then discuss fundamental RNA recognition principles along with methods for small molecule screening and RNA structure determination. Finally, we review unique challenges and emerging solutions from both the RNA and small molecule perspectives for generating RNA-targeted ligands before highlighting a selection of the "Greatest Hits" to date. These molecules target RNA in a variety of diseases, including cancer, neurodegeneration, and viral infection, in cellular and animal model systems. Additionally, we explore the recently FDA-approved small molecule regulator of RNA splicing, risdiplam, for treatment of spinal muscular atrophy. Together, this Tutorial Review showcases the fundamental role of chemical and molecular recognition principles in enhancing our understanding of RNA biology and contributing to the rapidly growing number of RNA-targeted probes and therapeutics. In particular, we hope this widely accessible review will serve as inspiration for aspiring small molecule and/or RNA researchers.

AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses

RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., the starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNA.

Drug development is a lengthy and complicated process, which requires costly experiments on a very large number of chemical compounds. The identification of chemical molecules with desired properties can be facilitated by computational methods. Several methods were developed for computer-aided design of drugs that target protein molecules. However, recently the ribonucleic acid (RNA) emerged as an attractive target for the development of new drugs. Unfortunately, the portfolio of the computer methods that can be applied to study RNA and its interactions with small chemical molecules is very limited. This situation motivated us to develop a new computational method, with which to predict RNA-small molecule interactions. To this end, we collected the information on the statistics of interactions in experimentally determined structures of complexes formed by RNA with small molecules. We then used the statistical data to train machine learning methods aiming to distinguish between RNA-ligand interactions observed experimentally and other interactions that can be observed in theoretical analyses, but are not observed in nature. The resulting method called AnnapuRNA is superior to other similar tools and can be used to predict preferred ligands of RNA molecules and how RNA and small molecules interact with each other.

RanDeL-Seq: a High-Throughput Method to Map Viral cis- and trans-Acting Elements

Recent studies have renewed interest in developing novel antiviral therapeutics and vaccines based on defective interfering particles (DIPs)—a subset of viral deletion mutants that conditionally replicate. Identifying and engineering DIPs require that viral cis- and trans-acting elements be accurately mapped.

It has long been known that noncoding genomic regions can be obligate cis elements acted upon in trans by gene products. In viruses, cis elements regulate gene expression, encapsidation, and other maturation processes, but mapping these elements relies on targeted iterative deletion or laborious prospecting for rare spontaneously occurring mutants. Here, we introduce a method to comprehensively map viral cis and trans elements at single-nucleotide resolution by high-throughput random deletion. Variable-size deletions are randomly generated by transposon integration, excision, and exonuclease chewback and then barcoded for tracking via sequencing (i.e., random deletion library sequencing [RanDeL-seq]). Using RanDeL-seq, we generated and screened >23,000 HIV-1 variants to generate a single-base resolution map of HIV-1’s cis and trans elements. The resulting landscape recapitulated HIV-1’s known cis-acting elements (i.e., long terminal repeat [LTR], Ψ, and Rev response element [RRE]) and, surprisingly, indicated that HIV-1’s central DNA flap (i.e., central polypurine tract [cPPT] to central termination sequence [CTS]) is as critical as the LTR, Ψ, and RRE for long-term passage. Strikingly, RanDeL-seq identified a previously unreported ∼300-bp region downstream of RRE extending to splice acceptor 7 that is equally critical for sustained viral passage. RanDeL-seq was also used to construct and screen a library of >90,000 variants of Zika virus (ZIKV). Unexpectedly, RanDeL-seq indicated that ZIKV’s cis-acting regions are larger than the untranscribed (UTR) termini, encompassing a large fraction of the nonstructural genes. Collectively, RanDeL-seq provides a versatile framework for generating viral deletion mutants, enabling discovery of replication mechanisms and development of novel antiviral therapeutics, particularly for emerging viral infections.