Targeting RNA structures with small molecules

Beyond DNA interactions: Insights into idarubicin's binding dynamics with tRNA using spectroscopic and computational approaches

Idarubicin (4-demethoxydaunomycin), a structural analogue of daunomycin derived from Streptomyces peucetius, exhibits enhanced anticancer efficacy due to the substitution of a methoxy group with a hydrogen atom. This study investigates the binding interactions of idarubicin with RNA using a multifaceted approach, including infrared (IR) spectroscopy, absorption spectroscopy, circular dichroism (CD), molecular docking, and molecular dynamics (MD) simulations. The IR results demonstrate significant binding to guanine and uracil, indicated by spectral shifts, while MD simulations reveal additional interactions with adenine, highlighting a flexible binding mechanism. The binding constant of the idarubicin-RNA complex was calculated to be K = 2.1 × 103 M-1, reflecting a strong affinity and stable interaction. Thermodynamic analysis shows that the negative Gibbs free energy (ΔG ∼ -4.57 kcal/mol) signifies spontaneous binding under physiological conditions. The binding free energy estimation was carried out to check the binding affinity, stability and interactions of the complex which was assessed through molecular dynamics simulations. The stability of the idarubicin-RNA complex is further supported by a hyperchromic effect observed in absorption spectroscopy, suggesting effective intercalation that enhances base exposure. The binding is driven by hydrogen bonding, π-π stacking interactions, and electrostatic forces, which collectively stabilize the complex. Notably, the conformational integrity of RNA is largely preserved, with key structural features remaining unchanged in both IR and CD analyses. Comparatively, idarubicin's interactions with RNA differ from those with DNA, where the latter shows more substantial conformational perturbations. These findings enhance our understanding of anthracycline functionality and provide valuable insights for developing novel analogues with improved efficacy and reduced side effects, informing future therapeutic strategies targeting RNA in cancer treatment.

Ligand-binding pockets in RNA and where to find them

RNAs are critical regulators of gene expression, and their functions are often mediated by complex secondary and tertiary structures. Structured regions in RNA can selectively interact with small molecules-via well-defined ligand-binding pockets-to modulate the regulatory repertoire of an RNA. The broad potential to modulate biological function intentionally via RNA-ligand interactions remains unrealized, however, due to challenges in identifying compact RNA motifs with the ability to bind ligands with good physicochemical properties (often termed drug-like). Here, we devise fpocketR, a computational strategy that accurately detects pockets capable of binding drug-like ligands in RNA structures. Remarkably few, roughly 50, of such pockets have ever been visualized. We experimentally confirmed the ligandability of novel pockets detected with fpocketR using a fragment-based approach introduced here, Frag-MaP, that detects ligand-binding sites in cells. Analysis of pockets detected by fpocketR and validated by Frag-MaP reveals dozens of sites able to bind drug-like ligands, supports a model for RNA secondary structural motifs able to bind quality ligands, and creates a broad framework for understanding the RNA ligand-ome.

Artificial intelligence for RNA-ligand interaction prediction: advances and prospects

Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, Artificial Intelligence (AI) is revolutionizing the study of RNA-ligand interactions, offering insights into the complex dynamics and therapeutic potential of RNA. In this review, we highlight advances in AI-driven RNA-ligand binding site identification, structure modeling, binding mode and binding affinity prediction, and virtual screening (VS). We also discuss challenges, such as data set scarcity and modeling the flexibility of RNA, are discussed. Future directions emphasize integrating cutting-edge AI techniques with physics-based models and expanding experimental data sets to enhance RNA-ligand interaction predictions.

Electrostatic Anchoring in RNA-Ligand Design─Dissecting the Effects of Positive Charges on Affinity, Selectivity, Binding Kinetics, and Thermodynamics

Targeting RNA with small molecules is an emerging field in medicinal chemistry. However, highly potent ligands are often challenging to achieve. One intuitive strategy to enhance ligand's potency is the implementation of positively charged moieties to interact with the negatively charged RNA phosphate backbone. We investigated the effect of such "electrostatic anchors" on binding affinity, kinetics, thermodynamics, and selectivity by MST, SPR, and ITC experiments, respectively, with the Ba SAM-VI riboswitch and the Tte preQ1 riboswitch aptamer model systems. RNA-ligand interactions were dominated by enthalpy, and electrostatic anchors had moderate effects on binding affinity driven by faster association rates for higher charged ligands. Despite the observations of loose binding interactions in SPR experiments with multibasic ligands, selectivity over structurally unrelated RNA off-targets was maintained. Therefore, the addition of positively charged moieties is no universal RNA-ligand design principle, but a purposefully implemented ionic RNA-ligand interaction can enhance potency without impairing selectivity.

19F NMR in RNA structural biology: exploring structures, dynamics, and small molecule interactions

RNA molecules play essential roles in numerous biological pathways, making them attractive targets for drug discovery. Despite the challenges in developing small molecules targeting RNA, the success in developing compounds that modulate RNA function underscores its therapeutic potential. 19F NMR spectroscopy has emerged as a powerful tool in structural biology and drug discovery, particularly for studying macromolecular structures and ligand interactions. As RNA continues to gain prominence as a drug target, 19F NMR is expected to play a pivotal role in advancing RNA-focused drug discovery. This review describes the diverse applications of 19F NMR in RNA biology, including its use in characterizing RNA structures, probing molecular dynamics, identifying small-molecule binders, and investigating interaction mechanisms of small-molecule ligands. By providing detailed structural and ligand binding insights, 19F NMR will facilitate the discovery of RNA-targeting therapeutics and deepen our understanding of RNA modulatory mechanisms.

Small-molecule RNA ligands: a patent review (2018-2024)

INTRODUCTION

Targeting three-dimensional RNA structures with traditional drug-like small molecules is gaining wide attention in both the academia and the pharmaceutical industries, due to their good oral bioavailability, cheap production cost, and the possibility of fine-tuning ADMET properties, which represent a powerful alternative to the current RNA-targeted therapies, including ASO and siRNA. As RNAs are involved in nearly all the physiological and pathological processes, small molecules RNA ligands can have a plethora of different therapeutic applications, spanning from cancer to infectious and neurological diseases.

AREAS COVERED

This review describes patents concerning small molecules RNA ligands published within January 2018 and October 2024, searched through Espacenet, Patentscope, and Google Patents databases.

EXPERT OPINION

The number of patents that has been released in the last few years demonstrates the relevance of targeting RNA structures for the development of next generation chemotherapeutic agents and antiviral/antibacterial drugs, even though this field is still in its infancy and many issues still need to be resolved, in particular related to selectivity. An emerging approach to considerably limiting side effects is presented by RIBOTAC derivatives, as promoting a selective RNase-L mediated RNA degradation allows to significantly reduce the dose of the compound.

Pentatricopeptide repeat protein targeting CUG repeat RNA ameliorates RNA toxicity in a myotonic dystrophy type 1 mouse model

Myotonic dystrophy type 1 (DM1) is an autosomal dominant multisystemic disorder caused by the expansion of a CTG-triplet repeat in the 3' untranslated region of the dystrophia myotonica protein kinase (DMPK) gene. It results in the transcription of toxic RNAs that contain expanded CUG repeats (CUGexp). Splicing factors, such as muscleblind-like 1 (MBNL1), are sequestered by CUGexp, thereby disrupting the normal splicing program that is essential for various cellular functions. Pentatricopeptide repeat (PPR) proteins, originally found in plants, regulate RNA in organelles by binding in a sequence-specific manner. Here, we designed PPR proteins that specifically bind to the hexamer of CUG repeat RNAs (CUG-PPRs) and showed that CUG-PPR1 could ameliorate RNA toxicity induced by CUGexp in cell models of DM1. A single systemic recombinant adeno-associated virus (AAV9) vector-mediated gene delivery of CUG-PPR1 demonstrated long-term therapeutic effects on myotonia and restored splicing activity in a mouse model of DM1. These results highlight the potential of PPR molecules to target pathogenic RNA sequences in DM1 and potentially other RNA-mediated disorders.

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models

RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule binding sites. However, accurate and efficient computational methods for identifying these interactions are still lacking. Recently, advances in large language models (LLMs), previously successful in DNA and protein research, have spurred the development of RNA-specific LLMs. These models leverage vast unlabeled RNA sequences to autonomously learn semantic representations with the goal of enhancing downstream tasks, particularly those constrained by limited annotated data. Here, we develop RNABind, an embedding-informed geometric deep learning framework to detect RNA-small molecule binding sites from RNA structures. RNABind integrates RNA LLMs into advanced geometric deep learning networks, which encodes both RNA sequence and structure information. To evaluate RNABind, we first compile the largest RNA-small molecule interaction dataset from the entire multi-chain complex structure instead of single-chain RNAs. Extensive experiments demonstrate that RNABind outperforms existing state-of-the-art methods. Besides, we conduct an extensive experimental evaluation of eight pre-trained RNA LLMs, assessing their performance on the binding site prediction task within a unified experimental protocol. In summary, RNABind provides a powerful tool on exploring RNA-small molecule binding site prediction, which paves the way for future innovations in the RNA-targeted drug discovery.

Genome-Wide Analysis of Stable RNA Secondary Structures across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics

Small molecules targeting specific RNA-binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, which is an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA secondary structure finder (R2S-Finder) to discover short, stable RNA structural motifs in humans, Escherichia coli (E. coli), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across the organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also validated that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent across different organisms, indicating that stable structure formation is mostly driven by RNA folding, while a larger variation was found between in vitro and in vivo data for certain RNA types, such as human long intergenic noncoding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist under both in vivo and in vitro conditions and can potentially serve as drug targets. All results of stable structures, stem-loops, internal loops, bulges, and n-way junctions have been collated in the R2S-Finder database (https://github.com/JingxinWangLab/R2S-Finder), which is coded in hyperlinked HTML pages and tabulated in CSV files.

The Druggable Transcriptome Project: From Chemical Probes to Precision Medicines

RNA presents abundant opportunities as a drug target, offering significant potential for small molecule medicine development. The transcriptome, comprising both coding and noncoding RNAs, is a rich area for therapeutic innovation, yet challenges persist in targeting RNA with small molecules. RNA structure can be predicted with or without experimental data, but discrepancies with the actual biological structure can impede progress. Prioritizing RNA targets supported by genetic or evolutionary evidence enhances success. Further, small molecules must demonstrate binding to RNA in cells, not solely in vitro, to validate both the target and compound. Effective small molecule binders modulate functional sites that influence RNA biology, as binding to nonfunctional sites requires recruiting effector mechanisms, for example degradation, to achieve therapeutic outcomes. Addressing these challenges is critical to unlocking RNA's vast potential for small molecule medicines, and a strategic framework is proposed to navigate this promising field, with a focus on targeting human RNAs.

Stacking Interactions of Druglike Heterocycles with Nucleobases

Stacking interactions contribute significantly to the interaction of small molecules with RNA, and harnessing the power of these interactions will likely prove important in the development of RNA-targeting inhibitors. To this end, we present a comprehensive computational analysis of stacking interactions between a set of 54 druglike heterocycles and the natural nucleobases. We first show that heterocycle choice can tune the strength of stacking interactions with nucleobases over a large range and that heterocycles favor stacked geometries that cluster around a discrete set of stacking loci characteristic of each nucleobase. Symmetry-adapted perturbation theory results indicate that the strengths of these interactions are modulated primarily by electrostatic and dispersion effects. Based on this, we present a multivariate predictive model of the maximum strength of stacking interactions between a given heterocycle and nucleobase that depends on molecular descriptors derived from the electrostatic potential. These descriptors can be readily computed using density functional theory or predicted directly from atom connectivity (e.g., SMILES). This model is used to predict the maximum possible stacking interactions of a set of 1854 druglike heterocycles with the natural nucleobases. Finally, we show that trivial modifications of standard (fixed-charge) molecular mechanics force fields reduce errors in predicted stacking interaction energies from around 2 kcal/mol to below 1 kcal/mol, providing a pragmatic means of predicting more reliable stacking interaction energies using existing computational workflows. We also analyze the stacking interactions between ribocil and a bacterial riboswitch, showing that two of the three aromatic heterocyclic components engage in near-optimal stacking interactions with binding site nucleobases.

Perturbation of mRNA splicing in liver cancer: insights, opportunities and challenges

Perturbation of mRNA splicing is commonly observed in human cancers and plays a role in various aspects of cancer hallmarks. Understanding the mechanisms and functions of alternative splicing (AS) not only enables us to explore the complex regulatory network involved in tumour initiation and progression but also reveals potential for RNA-based cancer treatment strategies. This review provides a comprehensive summary of the significance of AS in liver cancer, covering the regulatory mechanisms, cancer-related AS events, abnormal splicing regulators, as well as the interplay between AS and post-transcriptional and post-translational regulations. We present the current bioinformatic approaches and databases to detect and analyse AS in cancer, and discuss the implications and perspectives of AS in the treatment of liver cancer.

Synthesis and evolution of 16-membered macrolide carrimycin derivatives as a novel class of anti-HCoV-OC43 agents targeting viral FSE RNA

We first demonstrate that carrimycin, as an antibiotic, shows broad-spectrum anti-coronavirus activity by targeting frameshifting element (FSE) RNA. Herein, taking carrimycin as the lead, 26 new 16-membered macrolides were synthesized and evaluated for antiviral activity against coronavirus strains. Compound 2d exhibited the elevated antiviral efficacy against HCoV-OC43 and HCoV-229E with EC50 values of 0.85 μM and 1.45 μM by directly targeting coronaviral FSE RNA pseudoknot. Molecular simulations revealed that the introduction of a 4″-substituent transforms the macrocyclic core into U-shaped conformation, enabling the higher binding with FSE. Meanwhile, using thermal proteome profiling (TPP) technology, we identified DIS3L2 as a potential host target, which probably assisted 2d to exert the antiviral effect. Therefore, the 16-membered macrolides constituted a new class of RNA inhibitors against coronaviruses, and 2d owns a dual-target mechanism that acts on both viral FSE RNA and host DIS3L2.

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning

Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.

Structural Biology of RNA and Protein-RNA Complexes after AlphaFold3

Recent breakthroughs in AI-mediated protein structure prediction have significantly accelerated research and generated valuable hypotheses within the field of structural biology and beyond. Notably, AlphaFold2 has facilitated the determination of larger protein complexes for which only limited experimental data are available. De novo predictions can now be experimentally validated with relative ease compared to the pre-AlphaFold2 era. In May 2024, AlphaFold3 was launched with high expectations, promising the capability to accurately predict RNA structures and protein-RNA complexes - features that were absent in AlphaFold2. This review evaluates the extent to which AlphaFold3 fulfills this promise through specific examples. At present, AlphaFold3 falls short in reliably predicting RNA and protein-RNA complex structures, particularly for non-canonical interactions where training data remain scarce. As a result, users should exercise caution when using AlphaFold3 predictions as hypotheses generators for RNA and protein-RNA complex structures. In the interim, integrating AI-based predictors with data-driven docking tools is recommended to address these limitations. This approach can help bridge the gap until sufficient training data are available to enable the development of more reliable predictive algorithms.

fpocketR: A platform for identification and analysis of ligand-binding pockets in RNA

Small molecules that bind specific sites in RNAs hold promise for altering RNA function, manipulating gene expression, and expanding the scope of druggable targets beyond proteins. Identifying binding sites in RNA that can engage ligands with good physicochemical properties remains a significant challenge. fpocketR is a software package for identifying, characterizing, and visualizing ligand-binding sites in RNA. fpocketR was optimized, through comprehensive analysis of currently available RNA-ligand complexes, to identify pockets in RNAs able to bind small molecules possessing favorable properties, generally termed drug-like. Here, we demonstrate use of fpocketR to analyze RNA-ligand interactions and novel pockets in small and large RNAs, to assess ensembles of RNA structure models, and to identify pockets in dynamic RNA systems. fpocketR performs best with RNA structures visualized at high (≤3.5 Å) resolution, but also provides useful information with lower resolution structures and computational models. fpocketR is a powerful, freely available tool for discovery and analysis of ligand-binding pockets in RNA molecules.

Porphyrins as Chiroptical Conformational Probes for Biomolecules

Porphyrins are highly conjugated macrocyclic compounds that possess exceptional photophysical and chemical properties, progressively establishing themselves as versatile tools in the structural investigation of biomolecules. This review explores their role as chiroptical conformational probes, focusing on their interactions with DNA and RNA. The planar electron rich structure of porphyrin macrocycle that promote π-π interactions, their easy functionalization at the meso positions, and their capacity to coordinate metal ions enable their use in probing nucleic acid structures with high sensitivity. Emphasis is placed on their induced circular dichroism (ICD) signals in the Soret region, which provide precise diagnostic insights into binding mechanisms and molecular interactions. The review examines the interactions of porphyrins with various DNA structures, including B-, Z-, and A-DNA, single-stranded DNA, and G-quadruplex DNA, as well as less common structures like I-motif and E-motif DNA. The last part highlights recent advancements in the use of porphyrins to probe RNA structures, emphasizing binding behaviors and chiroptical signals observed with RNA G-quadruplexes, as well as the challenges in interpreting ICD signals with other RNA motifs due to their inherent structural complexity.

Computation-Enabled Structure-Based Discovery of Potent Binders for Small-Molecule Aptamers

Aptamers, functional nucleic acids recognized for their high target-binding affinity and specificity, have been extensively employed in biosensors, diagnostics, and therapeutics. Conventional screening methods apply evolutionary pressure to optimize affinity, while counter-selections are used to minimize off-target binding and improve specificity. However, aptamer specificity characterization remains limited to target analogs and experimental controls. A systematic exploration of the chemical space for aptamer-binding chemicals (targets) is crucial for uncovering aptamer versatility and enhancing target specificity in practical applications, a task beyond the scope of experimental approaches. To address this, we employed a high-throughput three-stage structure-based computational framework to identify potent binders for two model aptamers. Our findings revealed that the l-argininamide (L-Arm)-binding aptamer has a 31-fold higher affinity for the retromer chaperone R55 than for L-Arm itself, while guanethidine and ZINC10314005 exhibited comparable affinities to L-Arm. In another case, norfloxacin and difloxacin demonstrated over 10-fold greater affinity for the ochratoxin A (OTA)-binding aptamer OBA3 than OTA, introducing a fresh paradigm in aptamer-target interactions. Furthermore, pocket mutation studies highlighted the potential to tune aptamer specificity, significantly impacting the bindings of L-Arm or norfloxacin. These findings demonstrate the effectiveness of our computational framework in discovering potent aptamer binders, thereby expanding the understanding of aptamer-binding versatility and advancing nucleic acid-targeted drug discovery.

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.

Mapping small molecule-RNA binding sites via Chem-CLIP synergized with capillary electrophoresis and nanopore sequencing

Target validation and identification of binding sites are keys to the development of bioactive small molecules that target RNA. Herein, we describe optimized protocols to profile small molecule-RNA interactions and to define binding sites of the small molecules in RNAs using covalent chemistry. Various reactive modules appended to an RNA-binding small molecule were studied for cross-linking to the RNA target. Electrophilic modules, whether N-chloroethyl aniline or diazirine, have reactive profiles consistent with induced proximity; however, probes with N-chloroethyl aniline were more reactive and more specific than those with a diazirine cross-linking moiety. Depending upon the identity of the cross-linking module, covalent adducts with different nucleotides that are proximal to a small molecule's binding site were formed. The nucleotides where cross-linking occurred were elucidated by using two different platforms: (i) automated capillary electrophoresis that identified a binding site by impeding reverse transcriptase, or "RT stops"; and (ii) nanopore sequencing where the cross-link produces mutations in the corresponding complementary DNA formed by reverse transcriptase-polymerase chain reaction amplification of the cross-linked RNA. These approaches are broadly applicable to aid in the advancement of chemical probes targeting RNA, including identifying binding sites and using covalent chemistry to screen for RNA-binding molecules in a high throughput format.

Electrochemical Biosensors for the Detection of Exosomal microRNA Biomarkers for Early Diagnosis of Neurodegenerative Diseases

Early and precise diagnosis of neurodegenerative disorders like Alzheimer's (AD) and Parkinson's (PD) is crucial for slowing their progression and enhancing patient outcomes. Exosomal microRNAs (miRNAs) are emerging as promising biomarkers due to their ability to reflect the diseases' pathology, yet their low abundance poses significant detection hurdles. This review article delves into the burgeoning field of electrochemical biosensors, designed for the precise detection of exosomal miRNA biomarkers. Electrochemical biosensors offer a compelling solution, combining the sensitivity required to detect low-abundance biomarkers with the specificity needed to discern miRNA profiles distinctive to neural pathological states. We explore the operational principles of these biosensors, including the electrochemical transduction mechanisms that facilitate miRNA detection. The review also summarizes advancements in nanotechnology, signal enhancement, bioreceptor anchoring, and microfluidic integration that improve sensor accuracy. The evidence of their use in neurodegenerative disease diagnosis is analyzed, focusing on the clinical impact, diagnostic precision, and obstacles faced in practical applications. Their potential integration into point-of-care testing and regulatory considerations for their market entry are discussed. Looking toward the future, the article highlights forthcoming innovations that might revolutionize early diagnostic processes. Electrochemical biosensors, with their impressive sensitivity, specificity, and point-of-care compatibility, are on track to become instrumental in the early diagnosis of neurodegenerative diseases, possibly transforming patient care and prognosis.

Exploring chemical space for "druglike" small molecules in the age of AI

The announcement of 2024 Nobel Prize in Chemistry to Alphafold has reiterated the role of AI in biology and mainly in the domain of "drug discovery". Till few years ago, structure-based drug design (SBDD) has been the preferred experimental design in many academic and pharmaceutical R and D divisions for developing novel therapeutics. However, with the advent of AI, the drug design field especially has seen a paradigm shift in its R&D across platforms. If "drug design" is a game, there are two main players, the small molecule drug and its target biomolecule, and the rules governing the game are mainly based on the interactions between these two players. In this brief review, we will be discussing our efforts in improving the state-of-the-art technology with respect to small molecules as well as in understanding the rules of the game. The review is broadly divided into five sections with the first section introducing the field and the challenges faced and the role of AI in this domain. In the second section, we describe some of the existing small molecule libraries developed in our labs and follow-up this section with a more recent knowledge-based resource available for public use. In section four, we describe some of the screening tools developed in our laboratories and are available for public use. Finally, section five delves into how domain knowledge is improving the utilization of AI in drug design. We provide three case studies from our work to illustrate this work. Finally, we conclude with our thoughts on the future scope of AI in drug design.

Ligand-binding pockets in RNA, and where to find them

RNAs are critical regulators of gene expression, and their functions are often mediated by complex secondary and tertiary structures. Structured regions in RNA can selectively interact with small molecules - via well-defined ligand binding pockets - to modulate the regulatory repertoire of an RNA. The broad potential to modulate biological function intentionally via RNA-ligand interactions remains unrealized, however, due to challenges in identifying compact RNA motifs with the ability to bind ligands with good physicochemical properties (often termed drug-like). Here, we devise fpocketR, a computational strategy that accurately detects pockets capable of binding drug-like ligands in RNA structures. Remarkably few, roughly 50, of such pockets have ever been visualized. We experimentally confirmed the ligandability of novel pockets detected with fpocketR using a fragment-based approach introduced here, Frag-MaP, that detects ligand-binding sites in cells. Analysis of pockets detected by fpocketR and validated by Frag-MaP reveals dozens of newly identified sites able to bind drug-like ligands, supports a model for RNA secondary structural motifs able to bind quality ligands, and creates a broad framework for understanding the RNA ligand-ome.

Chemotranscriptomic profiling with a thiamine monophosphate photoaffinity probe

RNA is a multifaceted biomolecule with numerous biological functions and can interact with small molecule metabolites as exemplified by riboswitches. Here, we profile the Escherichia coli transcriptome on interactions with the metabolite Thiamine Monophosphate (TMP). We designed and synthesized a photoaffinity probe based on the scaffold of TMP and applied it to chemotranscriptomic profiling. Using next-generation RNA sequencing, several potential interactions between bacterial transcripts and the probe were identified. A remarkable interaction between the TMP probe and the well-characterized Flavin Mononucleotide (FMN) riboswitch was validated by RT-qPCR, and further verified with competition assays. Localization of the photocrosslinked nucleotides using reverse transcription and docking predictions of the probe suggested binding to the riboswitch aptamer. After examining binding of unmodified TMP to the riboswitch using SHAPE, we found selective yet moderate binding interactions, potentially mediated by the phosphate group of TMP. Lastly, TMP appeared to enhance gene expression of a reporter gene that is under riboswitch control, while the natural ligand FMN displayed an inhibitory effect, hinting at a potential biological role of TMP. This work showcases the possibility of chemotranscriptomic profiling to identify new RNA-small molecule interactions.

Regulation of gene expression through protein-metabolite interactions

Organisms have to adapt to changes in their environment. Cellular adaptation requires sensing, signalling and ultimately the activation of cellular programs. Metabolites are environmental signals that are sensed by proteins, such as metabolic enzymes, protein kinases and nuclear receptors. Recent studies have discovered novel metabolite sensors that function as gene regulatory proteins such as chromatin associated factors or RNA binding proteins. Due to their function in regulating gene expression, metabolite-induced allosteric control of these proteins facilitates a crosstalk between metabolism and gene expression. Here we discuss the direct control of gene regulatory processes by metabolites and recent progresses that expand our abilities to systematically characterize metabolite-protein interaction networks. Obtaining a profound map of such networks is of great interest for aiding metabolic disease treatment and drug target identification.

Synthesis of electrophile-tethered preQ1 analogs for covalent attachment to preQ1 RNA

The preQ1 cIass-I riboswitch aptamer can utilize 7-aminomethyl-7-deazaguanine (preQ1) ligands that are equipped with an electrophilic handle for the covalent attachment of the ligand to the RNA. The simplicity of the underlying design of irreversibly bound ligand-RNA complexes has provided a new impetus in the fields of covalent RNA labeling and RNA drugging. Here, we present short and robust synthetic routes for such reactive preQ1 and (2,6-diamino-7-aminomethyl-7-deazapurine) DPQ1 ligands. The readily accessible key intermediates of preQ0 and DPQ0 (both bearing a nitrile moiety instead of the aminomethyl group) were reduced to the corresponding 7-formyl-7-deazapurine counterparts. These readily undergo reductive amination to form the hydroxyalkyl handles, which were further converted to the haloalkyl or mesyloxyalkyl-modified target compounds. In addition, we report hydrogenation conditions for preQ0 and DPQ0 that allow for cleaner and faster access to preQ1 compared to existing routes and provide the novel compound DPQ1.

DNA Damaging Agents Induce RNA Structural and Transcriptional Changes for Genes Associated with Redox Homeostasis in Arabidopsis thaliana

Living organisms are constantly exposed to various DNA damaging agents. While the mechanisms of DNA damage and DNA repair are well understood, the impact of these agents on RNA secondary structure and subsequent function remains elusive. In this study, we explore the effects of DNA damaging reagent methyl methanesulfonate (MMS) on arabidopsis gene expression and RNA secondary structure using the dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq) method. Our analyses reveal that changes in transcriptional levels and mRNA structure are key factors in response to DNA damaging agents. MMS treatment leads to the up-regulation of arabidopsis RBOHs (respiratory burst oxidase homologues) and alteration in the RNA secondary structure of GSTF9 and GSTF10, thereby enhancing mRNA translation efficiency. Redox homeostasis manipulated by RBOHs and GSTFs plays a crucial role in MMS-induced primary root growth inhibition. In conclusion, our findings shed light on the effects of DNA damaging agents on RNA structure and potential mRNA translation, which provide a new insight to understand the mechanism of DNA damage.

Design, synthesis, and screening of an RNA optimized fluorinated fragment library

Fragment-based screening is an efficient method for early-stage drug discovery. In this study, we aimed to create a fragment library optimized for producing high hit rates against RNA targets. RNA has historically been an underexplored target, but recent research suggests potential for optimizing small molecule libraries for RNA binding. We extended this concept to fragment libraries to produce an RNA optimized fluorinated fragment library. We then screened this library, alongside two non-RNA optimized fragment libraries, against three RNA targets: the human cytoplasmic A-site and the S. cerevisiae tRNAAsp anticodon stem loop with and without nucleobase modifications. The screens yielded 24, 31, and 20 hits against the respective targets. Importantly, statistical analysis confirmed a significant overrepresentation of hits in our RNA optimized library. Based on these findings, we propose guidelines for developing RNA optimized fragment libraries. We hope the guidelines will help expediting fragment-based ligand discovery for RNA targets and contribute to presenting RNA as a promising target in drug discovery.

The first report of structural analysis of a nucleic acid using crystals grown in space

For the success of structure-based drug design, three-dimensional structures solved by X-ray crystallography at atomic resolution are mandatory. In order to obtain high-quality single crystals with strong diffraction power, crystallization under microgravity conditions has been attempted for proteins. Since nucleic acid duplexes have chemical, structural and crystallographic characteristics that differ from those of globular proteins, such as intermolecular repulsion due to negative charge and molecular and crystallographic anisotropies, it is interesting to investigate whether microgravity crystallization improves the crystal growth of nucleic acids. However, to our knowledge there has been only one report on nucleic acid crystallization in a microgravity environment, and there have been no reports of successful structural analysis. Here, we conducted the crystallization of a DNA/RNA heteroduplex in space. The heteroduplex was successfully crystallized in a microgravity environment, and the size and appearance of the crystals were improved compared with control experiments conducted on Earth. Although the effect of the counter-diffusion method is likely to be more significant than the effect of microgravity in this study, we were able to analyze the structure at a higher resolution (1.4 Å) than our previously reported crystal structure (1.9 Å).

PROTAC and Molecular Glue Degraders of the Oncogenic RNA Binding Protein Lin28

The interaction between proteins and RNA is crucial for regulating gene expression, with dysregulation often linked to diseases such as cancer. The RNA-binding protein (RBP) Lin28 inhibits the tumor suppressor microRNA (miRNA) let-7, making it a significant oncogenic factor in tumor progression and metastasis. In this study, a small molecule is used that binds Lin28 and blocks its inhibition of let-7. To enhance its efficay, the inhibitor is transformed into degraders via two degradation approaches: Proteolysis Targeting Chimera (PROTAC) and molecular glue. A series of PROTAC bifunctional molecules and molecular glues capable of degrading Lin28 in cells.is developed Both strategies significantly reduce overexpressed Lin28 and alleviate cancer cellular phenotypes. Notably, the molecular glue approach demonstrates exceptional potency, surpassing PROTAC in several aspects. This outcome underscores the superior efficiency of the molecular glue approach for targeted Lin28 degradation and highlights its potential for addressing associated diseases with small molecules. Innovative small molecule strategies such as molecular glue and PROTAC technology for targeted RBP degradation, hold promise for opening new avenues in RNA modulation and addressing related diseases.

Identification of chemical inhibitors targeting long noncoding RNA through gene signature-based high throughput screening

Scalable methods for functionally high-throughput screening of RNA-targeting small molecules are currently limited. Here, an RNA knockdown gene signature and high-throughput sequencing-based high-throughput screening (HTS2) were integrated to identify RNA-targeting compounds. We first generated a gene signature characterizing the knockdown of the long non-coding RNA LINC00973. Then, screening of 8199 compounds by HTS2 assay identified that treatments of Hesperadin and GSK1070916 significantly mimic the expression pattern of the LINC00973 knockdown gene signature. Functionally, cell phenotype changes after treatments of these two compounds also mimic the losing function of LINC00973 in multiple types of cancer cells. Mechanistically, the inhibitory action of these two compounds on LINC00973 primarily operates via the AURKB-mediated MAPK signaling pathway, resulting in reduced expression of the transcription factor c-Jun. Consequently, this leads to the suppression of LINC00973 transcription. Moreover, these two compounds significantly inhibit xenograft tumor growth in vivo. Clinically, we further found that breast tumors with high expression of LINC00973 also show relatively high expression of AURKB or JUN, and vice versa. In summary, we established a novel high-throughput screening strategy to identify small molecules capable of targeting RNA, provided two promising compounds targeting LINC00973 and further shed light on the underlying transcriptional upregulation mechanism of LINC00973 within cancer cells.

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.

Exploring the therapeutic potential of modulating nonsense-mediated mRNA decay

Discovered more than four decades ago, nonsense-mediated mRNA decay (NMD) plays a fundamental role in the regulation of gene expression and is a major contributor to numerous diseases. With advanced technologies, several novel approaches aim to directly circumvent the effects of disease-causing frameshift and nonsense mutations. Additional therapeutics aim to globally dampen the NMD pathway in diseases associated with pathway hyperactivation, one example being Fragile X syndrome. In other cases, therapeutics have been designed to hijack or inhibit the cellular NMD machinery to either activate or obviate transcript-specific NMD by modulating pre-mRNA splicing. Here, we discuss promising approaches employed to regulate NMD for therapeutic purposes and highlight potential challenges in future clinical development. We are optimistic that the future of developing target-specific and global modulators of NMD (inhibitors as well as activators) is bright and will revolutionize the treatment of many genetic disorders, especially those with high unmet medical need.

Small molecules reveal differential shifts in stability and protein binding for G-quadruplex RNA

The potential of therapeutically targeting RNA with small molecules continues to grow yet progress is hindered by difficulties in determining specific mechanisms of action, including impacts on RNA-protein binding. RNA G-quadruplexes (rGQs) are a particularly promising target due to their range of biological functions, structural stability, and hydrophobic surfaces, which promote small molecule and protein interactions alike. Challenges arise due to 1) the low structural diversity among rGQs, thereby limiting binding selectivity, and 2) a lack of knowledge regarding how small molecules can manipulate rGQ-protein binding on a global scale. We first leveraged a small molecule library privileged for RNA tertiary structures that displayed differential binding to rGQs based on loop length, consistent with computational predictions for DNA GQs. We next utilized an RT-qPCR-based assay to measure stability against enzymatic readthrough, expected to be a common mechanism in rGQ function. We discovered small molecules with significant, bidirectional impacts on rGQ stability, even within the same scaffold. Using Stability of Proteins from Rates of Oxidation (SPROX), a stability-based proteomics method, we then elucidated proteome level impacts of both stabilizing and destabilizing rGQ-targeting molecules on rGQ-protein interactions. This technique revealed small molecule-induced impacts on a unique subset of rGQ-binding proteins, along with proteins that exhibited differential changes based on the identity of the small molecule. The domain and peptide-level insights resulting from SPROX allow for the generation of specific hypotheses for both rGQ function and small molecule modulation thereof. Taken altogether, this methodology helps bridge the gap between small molecule-RNA targeting and RNA-protein interactions, providing insight into how small molecules can influence protein binding partners through modulation of target RNA structures.

NesT-NABind: a Nested Transformer for Nucleic Acid-Binding Site Prediction on Protein Surface

Protein-nucleic acid interactions play a crucial role in many physiological processes. Identifying the binding sites of nucleotides on the protein surface is the prerequisite for understanding the molecular recognition mechanisms between the two types of macromolecules and also provides the information to design or generate molecule modulators against these sites to manipulate biological function according to specific requirements. Existing studies mainly focus on characterizing local surfaces around sites, often neglecting the interrelationships among these sites and the global protein information. To address this gap, we propose NesT-NABind, a Nested Transformer for Nucleic Acid-Binding site prediction. This model leverages the Transformer's advanced capabilities in contextual understanding and long-range dependency capturing. Specifically, we introduce a local patch-scale Transformer to process surface information around each site and a global protein-scale transformer to integrate surface and sequence information on the entire protein. These two Transformers operate at different scales of protein, hence the term "nested". Experiments demonstrate that NesT-NABind achieves a 5.57% improvement in the F1 score and a 3.64% improvement in AUPRC compared to state-of-the-art methods. With the incorporation of global features, NesT-NABind shows an enhanced predictive capability for the challenging large proteins and therefore can be used in a much wider range of applications.

Accurate Physics-Based Prediction of Binding Affinities of RNA- and DNA-Targeting Ligands

Accurate prediction of the affinity of ligand binding to nucleic acids represents a formidable challenge for current computational approaches. This limitation has hindered the use of computational methods to develop small-molecule drugs that modulate the activity of nucleic acids, including those associated with anticancer, antiviral, and antibacterial effects. In recent years, significant scientific and technological advances as well as easier access to compute resources have contributed to free-energy perturbation (FEP) becoming one of the most consistently reliable approaches for predicting relative binding affinities of ligands to proteins. Nevertheless, FEP's applicability to nucleic-acid targeting ligands has remained largely undetermined. In this work, we present a systematic assessment of the accuracy of FEP, as implemented in FEP+ software and facilitated by the OPLS4 force field, in predicting relative binding free energies of congeneric series of ligands interacting with a variety of DNA/RNA systems. The study encompassed more than 100 ligands exhibiting diverse binding modes, some partially exposed and others deeply buried. Using a consistent simulation protocol, more than half of the predictions are within 1 kcal/mol of the experimentally measured values. Across the data set, we report a combined average pairwise root-mean-square-error of <1.4 kcal/mol, which falls within one log unit of the experimentally measured dissociation constants. These results suggest that FEP+ has sufficient accuracy to guide the optimization of lead series in drug discovery programs targeting RNA and DNA.

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

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

Stimulus-activated ribonuclease targeting chimeras for tumor microenvironment activated cancer therapy

RNA degradation using ribonuclease targeting chimeras (RiboTACs) is a promising approach for cancer therapy. However, potential off-target degradation is a serious issue. Here, a RiboTAC is designed for tumor microenvironment triggered activation. The tumor microenvironment activated RiboTAC (TaRiboTAC) incorporates two pre-miR-21 binders, a near-infrared fluorophore IR780, an RGD targeting peptide and a phenylboronic acid caged ribonuclease recruiter. The caged ribonuclease recruiter is embedded in the molecule and exposed in acidic pH, the phenylboronic acid cage is removed by H2O2 making the TaRiboTAC responsive to the acidic and high H2O2 levels in the tumor microenvironment. It is shown the TaRiboTAC targets tumor tissue and degrades pre-miR-21. The degradation of pre-miR-21 by TaRiboTACs significantly increases the radiotherapeutic susceptibility of cancer cells achieving efficient suppression of human lung adenocarcinoma A549 tumors in living mice.

Designing small molecules that target a cryptic RNA binding site via base displacement

Most RNA-binding small molecules have limited solubility, weak affinity, and/or lack of specificity, restricting the medicinal chemistry often required for lead compound discovery. We reasoned that conjugation of these unfavorable ligands to a suitable "host" molecule can solubilize the "guest" and deliver it site-specifically to an RNA of interest to resolve these issues. Using this framework, we designed a small molecule library that was hosted by cobalamin (Cbl) to interact with the Cbl riboswitch through a common base displacement mechanism. Combining in vitro binding, cell-based assays, chemoinformatic modeling, and structure-based design, we unmasked a cryptic binding site within the riboswitch that was exploited to discover compounds that have affinity exceeding the native ligand, antagonize riboswitch function, or bear no resemblance to Cbl. These data demonstrate how a privileged biphenyl-like scaffold effectively targets RNA by optimizing π-stacking interactions within the binding pocket.

RNA folding kinetics control riboswitch sensitivity in vivo

Riboswitches are ligand-responsive gene-regulatory RNA elements that perform key roles in maintaining cellular homeostasis. Understanding how riboswitch sensitivity to ligand (EC50) is controlled is critical to explain how highly conserved aptamer domains are deployed in a variety of contexts with different sensitivity demands. Here we uncover roles by which RNA folding dynamics control riboswitch sensitivity in cells. By investigating the Clostridium beijerinckii pfl ZTP riboswitch, we identify multiple mechanistic routes of altering expression platform sequence and structure to slow RNA folding, all of which enhance riboswitch sensitivity. Applying these methods to riboswitches with diverse aptamer architectures and regulatory mechanisms demonstrates the generality of our findings, indicating that any riboswitch that operates in a kinetic regime can be sensitized by slowing expression platform folding. Our results add to the growing suite of knowledge and approaches that can be used to rationally program cotranscriptional RNA folding for biotechnology applications, and suggest general RNA folding principles for understanding dynamic RNA systems in other areas of biology.

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.

Recognition of Noncanonical RNA Base Pairs Using Triplex-Forming Peptide Nucleic Acids

Noncanonical base pairs play an important role in enabling the structural and functional complexity of RNA. Molecular recognition of such motifs is challenging because of their diversity, significant deviation from the Watson-Crick structures, and dynamic behavior, resulting in alternative conformations of similar stability. Triplex-forming peptide nucleic acids (PNAs) have emerged as excellent ligands for the recognition of Watson-Crick base-paired double helical RNA. The present study extends the recognition potential of PNA to RNA helices having noncanonical GoU, AoC, and tandem GoA/AoG base pairs. The purines of the noncanonical base pairs formed M+·GoU, T·AoC, M+·GoA, and T·AoG Hoogsteen triples of similar or slightly reduced stability compared to the canonical M+·G-C and T·A-U triples. Recognition of pyrimidines was more challenging. While the P·CoA triple was only slightly less stable than P·C-G, the E nucleobase did not form a stable triple with U of the UoG wobble pair. Molecular dynamics simulations suggested the formation of expected Hoogsteen hydrogen bonds for all of the stable triples. Collectively, these results expand the scope of triple helical recognition to noncanonical structures and sequence motifs common in biologically relevant RNAs.

Crystallographic analysis of G-clamp-RNA complex assisted by large scale RNA-binding profile

We present the X-ray crystal structure of a complex between a G-clamp and an internal loop motif of pre-mir-125a, selected from high affinity RNAs identified in a large-scale RNA-binding profile. This X-ray crystal structure reveals that the G-clamp interacts with three distinct guanine bases, forming robust bonds through hydrogen bonding and stacking interactions.

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

One of the hallmarks of RNA viruses is highly structured untranslated regions (UTRs) which are often essential for viral replication, transcription, or translation. In this report, we discovered a series of coumarin derivatives that bind to a four-way RNA helix called SL5 in the 5' UTR of the SARS-CoV-2 RNA genome. To locate the binding site, we developed a sequencing-based method namely cgSHAPE-seq, in which an acylating probe was directed to crosslink with the 2'-OH group of ribose at the binding site to create read-through mutations during reverse transcription. cgSHAPE-seq unambiguously determined a bulged G in SL5 as the primary binding site, which was validated through mutagenesis and in vitro binding experiments. The coumarin derivatives were further used as a warhead in designing RNA-degrading chimeras to reduce viral RNA expression levels. The optimized RNA-degrading chimera C64 inhibited live virus replication in lung epithelial carcinoma cells.

Use of a small molecule microarray screen to identify inhibitors of the catalytic RNA subunit of Methanobrevibacter smithii RNase P

Despite interest in developing therapeutics that leverage binding pockets in structured RNAs-whose dysregulation leads to diseases-such drug discovery efforts are limited. Here, we have used a small molecule microarray (SMM) screen to find inhibitors of a large ribozyme: the Methanobrevibacter smithii RNase P RNA (Msm RPR, ∼300 nt). The ribonucleoprotein form of RNase P, which catalyzes the 5'-maturation of precursor tRNAs, is a suitable drug target as it is essential, structurally diverse across life domains, and present in low copy. From an SMM screen of 7,300 compounds followed by selectivity profiling, we identified 48 hits that bound specifically to the Msm RPR-the catalytic subunit in Msm (archaeal) RNase P. When we tested these hits in precursor-tRNA cleavage assays, we discovered that the drug-like M1, a diaryl-piperidine, inhibits Msm RPR (KI, 17 ± 1 μM) but not a structurally related archaeal RPR, and binds to Msm RPR with a KD(app) of 8 ± 3 μM. Structure-activity relationship analyses performed with synthesized analogs pinpointed groups in M1 that are important for its ability to inhibit Msm RPR. Overall, the SMM method offers prospects for advancing RNA druggability by identifying new privileged scaffolds/chemotypes that bind large, structured RNAs.

RASP v2.0: an updated atlas for RNA structure probing data

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

Identification and characterization of shifted G•U wobble pairs resulting from alternative protonation of RNA

RNA can serve as an enzyme, small molecule sensor, and vaccine, and it may have been a conduit for the origin of life. Despite these profound functions, RNA is thought to have quite limited molecular diversity. A pressing question, therefore, is whether RNA can adopt novel molecular states that enhance its function. Covalent modifications of RNA have been demonstrated to augment biological function, but much less is known about non-covalent alterations such as novel protonated or tautomeric forms. Conventionally, a G•U wobble has the U shifted into the major groove. We used a cheminformatic approach to identify four structural families of shifted G•U wobbles in which the G instead resides in the major groove of RNA, which requires alternative tautomeric states of either base, or an anionic state of the U. We provide experimental support for these shifted G•U wobbles via the potent, and unconventional, in vivo reactivity of the U with dimethylsulfate (DMS) in three organisms. These shifted wobbles may play important functional roles and could serve as drug targets. Our cheminformatics approach is general and can be applied to identify alternative protonation states in other RNA motifs, as well as in DNA and proteins.

Determining structures of RNA conformers using AFM and deep neural networks

Much of the human genome is transcribed into RNAs1, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded2-are conformationally heterogeneous and flexible, which is a prerequisite for function3,4, but this limits the applicability of methods such as NMR, crystallography and cryo-electron microscopy for structure elucidation. Moreover, owing to the lack of a large RNA structure database, and no clear correlation between sequence and structure, approaches such as AlphaFold5 for protein structure prediction do not apply to RNA. Therefore, determining the structures of heterogeneous RNAs remains an unmet challenge. Here we report holistic RNA structure determination method using atomic force microscopy, unsupervised machine learning and deep neural networks (HORNET), a novel method for determining three-dimensional topological structures of RNA using atomic force microscopy images of individual molecules in solution. Owing to the high signal-to-noise ratio of atomic force microscopy, this method is ideal for capturing structures of large RNA molecules in distinct conformations. In addition to six benchmark cases, we demonstrate the utility of HORNET by determining multiple heterogeneous structures of RNase P RNA and the HIV-1 Rev response element (RRE) RNA. Thus, our method addresses one of the major challenges in determining heterogeneous structures of large and flexible RNA molecules, and contributes to the fundamental understanding of RNA structural biology.