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Targeting RNA structures with small molecules
Abstract
RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing — by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade. The potential of therapeutically targeting RNA structures with small molecules is being increasingly recognized. Here, Disney and colleagues review strategies to identify, validate and optimize small-molecule RNA binders. Examples of existing RNA-targeted small molecules, as well as challenges and future directions in the field, are discussed.
... RNA has the ability to form complex structures and conformations which can be exploited by targeted drug design [1][2][3][4]. Riboswitches are regions within the untranslated sequence of certain bacterial mRNA genes that can act as powerful gene regulatory elements [5]. Riboswitches are a class of noncoding RNA that do not require proteins for their function. ...
... Synthesis: Reactions were performed in oven-dried flasks or glass microwave vessels and those requiring an inert atmosphere were conducted under high purity argon. 1 H and 13 C NMR spectra were measured with a Varian MR-400 MHz Spectrometer and referenced to CHCl 3 at 7.26 ppm and 77.0 ppm, respectively, or DMSO at 2.50 ppm. Thin layer chromatography was performed on 60-mesh silica plates purchased from Sorbent Technologies (XHL, UV254, 250mm). ...
... The resulting solid was filtered hot and collected to yield 19.7 g (91% yield) of a fine white powder. 1 Synthesis of 2-acetamido-6-chloro-9-(2′,3′,5′-tri-O-acetyl-β-D-ribofuranosyl)purine (SK3) DMAP (2.38g, 19.50 mmol) and SK2 (1.67 g, 3.90 mmol) were added to a round bottom flask equipped with a stir bar and flushed with argon for 10 minutes. Anhydrous CH 2 Cl 2 (42 mL) was added followed by a slow addition of acetyl chloride (19.50 mmol, 1.39 mL) and pyridine (19.50 mmol, 1.57 mL). ...
Riboswitches are structured elements predominantly found in the 5’-untranslated region of many bacterial mRNA. These noncoding RNA regions play a vital role in bacterial metabolism and overall function. Each riboswitch binds to a specific small molecule and causes conformational changes in the mRNA leading to regulation of transcription or translation. In this work, we have synthesized SK4, a novel nucleoside analog that binds to the guanine riboswitch mRNA of the xanthine phosphoribosyl transferase gene in Bacillus subtilis and terminates transcription of the riboswitch mRNA to a greater extent than the native ligand guanine. Molecular dynamics simulations of SK4 with riboswitch mRNA reveal an overall stable complex with additional bonding interactions in comparison to guanine. Our work with SK4 demonstrates that specific genes in bacteria can be effectively controlled by ligand analogs, providing an alternative mechanism to regulate the function of bacteria.
... There is also intense debate regarding what kinds of specific RNA structures are capable of harboring pockets able to bind ligands with favorable physicochemical properties. The current dialogue centers on the relative importance of targeting simple stem-loop or bulge containing motifs, which may have stronger current biological validation, versus targeting complex RNA motifs which can potentially form more selective interactions with small molecules, but are harder to identify (3,(18)(19)(20). Most recent work has focused on the former class of simple RNA targets. ...
... Despite these clear advantages of targeting complex structures, a significant component of current work directed at discovery of RNA-targeting small molecules focuses on RNA motifs with simple structures (reviewed in (3,(18)(19)(20)). Our study indicates that simple bulge and consecutive loop motifs contain pockets only infrequently (Fig. 8A). ...
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.
... Developing scoring methods that generalize beyond the available training data is especially important for the next generation of drugs. Recent advances in drug discovery have expanded interest beyond the traditional druggable proteome, targeting intrinsically disordered proteins [38], protein-protein interactions [33], and RNA tertiary structures [12] as promising avenues for nextgeneration therapeutics. The ability to efficiently identify small-molecule binders for these novel targets could greatly accelerate the development of new treatments. ...
Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training target-specific neural networks on molecular dynamics simulations of the protein in complex with random small molecules. Our method uses force matching to learn an implicit free energy landscape of ligand binding for each target. Evaluated on six proteins, our approach achieves competitive virtual screening performance using 100-500 s of MD simulations per target. Notably, this approach achieves state-of-the-art early enrichment when using the true pose for active compounds. These results highlight the potential of physics-informed learning for virtual screening on novel targets. We publicly release the code for this paper at https://github.com/molecularmodelinglab/lfm.
... The existence of high-quality structural information coupled with uniquely valuable imaging applications makes the Mango aptamers an attractive system for structure-informed ligand design against RNA. Recognition of RNA as an important target for small molecules is increasing [28][29][30][31] . However, strategies to develop potent, selective small-molecule ligands for RNA still lag far behind protein-targeting strategies. ...
RNA-based fluorogenic aptamers, such as Mango, are uniquely powerful tools for imaging RNA that activate the fluorescence of a weakly or non-fluorescent small molecule when bound. A central challenge has been to develop brighter, more specific and high-affinity aptamer–ligand systems for cellular imaging. Here we report an ultrabright fluorophore for the Mango II system discovered using a structure-informed, fragment-based small-molecule microarray approach. This dye—termed SALAD1 (structure-informed, array-enabled LigAnD 1)—exhibits subnanomolar aptamer affinity and 3.5-fold brighter fluorescence than Mango II-TO1–biotin pair, a widely used fluorogenic system. Performance was improved by modulating RNA-dye molecular recognition without altering the fluorophore’s π-system. High-resolution X-ray structures reveal the binding mode for SALAD1, which exhibits improved pocket occupancy, a more defined binding pose and a unique bonding interaction with potassium. SALAD1 is cell-permeable and facilitates improved in-cell confocal RNA imaging. This work introduces an additional RNA-activated fluorophore demonstrating how fragment-based ligand discovery can be used to create high-performance ligands for RNA targets.
... RNA-targeted small molecules regulate non-coding RNA-mediated transcription and translation through multiple mechanisms. A growing library of RNA-targeted small molecules suggests that RNA is indeed amenable to pharmacological treatment, and several compounds are already in clinical trials or have been approved [205]. Synergizing these approaches-where CRISPR achieves precision editing and small molecules enable reversible modulation-could overcome current specificity and delivery barriers. ...
In recent years, the interplay between N6-methyladenosine (m6A) modifications and non-coding RNAs (ncRNAs) has emerged as a pivotal research area, owing to their crucial involvement in the pathophysiological mechanisms underlying various diseases. A significant hurdle in cancer therapy is therapeutic resistance, which frequently contributes to adverse patient outcomes. Recent investigations have underscored the vital role that interactions between m6A modifications and ncRNAs play in mediating cancer therapeutic resistance via the MAPK, PI3K/Akt/mTOR, Wnt/β-catenin, HIPPO, and NF-κB pathways. This review elucidates how these interactions drive tumor therapeutic resistance by modulating these pathways. By dissecting the regulatory dynamics between m6A and ncRNAs in the context of cancer therapeutic resistance, this review aims to deepen the understanding of m6A-ncRNA interaction in cancer therapeutic resistance and identify potential therapeutic targets to improve cancer treatment efficacy.
... Sun et al., in their review, mainly explained the experimental technologies for drug screening to determine the novel drug targets and included a Section discussing some of the deep learning approaches to predict associations of small molecules with miRNAs and proteins [33]. Childs-Disney et al. have discussed various challenges of targeting RNA, such as its structural complexity and dynamic nature, and were mainly focused on the structurebased identification of SMs that target RNA molecules [34]. The review by Chen et al. was the earliest review explicitly summarizing ML methods for SMA predictions [17]. ...
MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM–miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM–miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.
... In the past decade, research and clinical development based on the physiological functions of RNAs, especially the ncRNAs, have been on the rise [49][50][51][52] . It is well known that structure is the basis of function. ...
Given usefulness of protein language models (LMs) in structure and functional inference, RNA LMs have received increased attentions in the last few years. However, these RNA models are often not compared against the same standard. Here, we divided RNA LMs into three classes (pretrained on multiple RNA types (especially noncoding RNAs), specific-purpose RNAs, and LMs that unify RNA with DNA or proteins or both) and compared 13 RNA LMs along with 3 DNA and 1 protein LMs as controls in zero-shot prediction of RNA secondary structure and functional classification. Results shows that the models doing well on secondary structure prediction often perform worse in function classification or vice versa, suggesting that more balanced unsupervised training is needed.
... 95,96 Developing such approaches broadly for RNA targets could therefore have significant potential. 3 Yet, there have been relatively few studies of small molecules that covalently target RNA. 23, 97, 98 One of the challenges with specific reactivity with RNA targets is that the bases have relatively similar reactivity profiles, unlike amino acid side chains which vary significantly. ...
RNA is a key drug target that can be modulated by small molecules, however covalent binders of RNA remain largely unexplored. Using a high-throughput mass spectrometry screen of 2,000 electrophilic compounds, we identified ligands that react with RNA in a binding-dependent manner. RNA reactivity was influenced by both the reactive group and the RNA-binding scaffold. Electrophilic modules such as 3-chloropivalamide, bis(2-chloroethyl)amine, chloroacetamide, and N-acylimidazole that react with proteins also cross-linked to RNA, especially when paired with aromatic heterocycles, particularly those with a thieno[3,2-c]pyridinium core. These results suggest that electrophiles commonly used for protein targeting can also covalently modify RNA, potentially contributing to both on- and off-target effects. This insight enabled the design of an RNA-specific covalent compound by modifying a Hoechst scaffold, originally identified to bind DNA, to react selectively with the expanded triplet repeat RNA, r(CUG) exp , that causes myotonic dystrophy type 1 (DM1). Selectivity appears to arise from binding to the RNA major groove near the reactive site. Overall, this study highlights the potential of rationally designing covalent RNA-targeting small molecules.
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... On the basis of the molecular pathomechanism of DM1, potential curative therapies have been explored in multiple modalities: antisense oligonucleotides (ASOs) (11,12); artificial site-specific RNA endonucleases (ASREs), RNA-targeting Cas9 and small molecules with ribonuclease (RNase) activity to deplete CUG exp (13)(14)(15); nuclease-dead Cas9 and small molecules to suppress the expression of CUG exp (16)(17)(18); and RNA binding small molecules (19)(20)(21)(22)(23) as well as zinc finger RNA binding protein (24) to dissociate the splicing factors from CUG exp . However, several issues, such as weak potency and short duration of pharmacological activity, on-and off-target toxicity, and immunogenicity, limit the clinical application of these modalities (25)(26)(27)(28)(29). ...
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 (CUG exp ). Splicing factors, such as muscleblind-like 1 (MBNL1), are sequestered by CUG exp , 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 CUG exp 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.
... cgi). RNAFold predicts hypothetical secondary structure of single stranded RNA on the basis of minimum free energy (MFE) and partition function using dynamic programming algorithm (Childs-Disney et al. 2022;Gruber et al. 2008;Mathews et al. 2004). The sequence of the entire vaccine construct was given as the input in FASTA format. ...
Listeria monocytogenes is a food-borne pathogen responsible for causing listeriosis with severe consequences in expectant women and immunodeficient individuals due to age, viral infections, or transplants. Despite its alarming mortality rate of 21–50%, there is currently no appropriate medication or protective measure available to prevent the infection in humans. In this context, our current research intends to devise a proficient anti-listeriosis vaccine through thoughtful exploration of reverse vaccinology tools. We examined 368 protein sequences of L. monocytogenes strain CLIP80459 and culled 29 of them as the most potent immunogens. We then followed a stringent subtractive selection strategy to identify 11 cytotoxic T-cell, 9 helper T-cell, and 8 linear B-cell epitopes from the preselected antigens, based on multiple relevant structural, chemical, and immunological features and population coverage. We merged these epitopes using appropriate linkers and included an adjuvant to create the fused peptide vaccine. The physico-chemical and immunological properties of the chimeric peptide were modelled and analyzed, revealing it to be stable, non-toxic, non-allergenic, and highly soluble. Additional investigations involving molecular docking studies followed by molecular dynamics simulation and immune simulation revealed that the designed vaccine is adequately immunogenic and capable of stable, extensive interactions with HLA and TLR2, leading to activation of humoral and cell-mediated immunity. The peptide’s suitability for recombinant expression and simple purification using an E. coli host was demonstrated through in silico cloning studies. Thus, our study led to the development of a preventive yet safe vaccine against listeriosis that awaits wet-lab validation.
... We see that oxaliplatin is a more effective RNA:protein crosslinker than 5FU and that temozolomide has a wider effect on the epitranscriptome. Thus, our work indicates that direct targeting of RNA or its epitranscriptome, both focuses of drug development programs of the 21 st century [55][56][57] , have been contributing to clinical benefit for decades. Learning from classic chemotherapies may identify where to apply technological advances for specific RNA targeting in the future and thereby reduce collateral effects. ...
RNA is subject to many modifications, from small chemical changes such as methylation through to conjugation of biomolecules such as glycans. As well as these endogenously written modifications, RNA is also exposed to damage induced by its environment. Certain clinical compounds are known to drive covalent modifications of RNA with a growing appreciation for how these affect function. To understand the regulation of these modifications we need a reliable, sensitive and rapid methodology for their quantification. Thus, we developed AquIRE and applied it to the analysis of drug-induced RNA damage, showing this to be widespread with intricate temporal dynamics. Using the same methodology we identify RNA:protein crosslinking and the rewriting of the epitranscriptome as a consequence of clinical RNA damage. We also demonstrate how liquid-liquid phase separation increases RNA damage and expand the horizons of the glycoRNA world across the kingdoms of life and into cell-free glycoRNA.
... The development of these tools account for unique characteristics of RNA-binding compounds, which typically exhibit lower octanol-water partition coefficients, greater topological polar surface areas, and more hydrogen bond donors and acceptors compared to protein-binding compounds (Childs- Disney et al., 2022). Despite these advances, the field faces limitations due to the relatively small number of available RNA structures for training deep learning models (Kozlovskii and Popov, 2021). ...
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.
... The vast scope of biological functions that can be modulated by altering RNA structure, translatability, or intermolecular interactions makes RNA an enticing target for small-molecule ligands (3)(4)(5)(6)(7)(8). The field has made progress in targeting RNA with a few successful humandevised small molecules, currently limited to linezolid, an antibiotic that binds the ribosome (9), and risdiplam and branaplam, splicing modifiers that bind pre-messenger RNA (10). ...
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 higher resolutions, 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.
... Therefore, obtaining direct data on RNA structure in vivo is essential for understanding how RNA structure contributes to RNA function. Several approaches have been developed to interrogate specific structural features of RNA molecules [22][23][24]. Traditional methods such as X-ray crystallography, nuclear magnetic resonance, and cryogenic electron microscopy are time-consuming and limited in predicting RNA structure in vivo. In recent years, methods based on biochemical or chemical probes have been developed [24][25][26]. ...
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.
... This understanding also has significant implications for drug development. Several compounds have been designed to modulate RNA-binding protein interactions 91,92 . For instance, inhibitors targeting the RNA-binding protein HuR, such as the small molecule MS-444, prevent HuR from binding to its RNA targets, which has shown promise in reducing tumor growth in certain cancers 93 and small molecule modulators of splicing, such as Risidiplam that has been approved for the treatment of spinal muscular atrophy 94 . ...
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.
... While "covalent drugs" have become a leading principle in medicinal chemistry in the "protein world" [35,36] -approximately 30% of all FDAapproved drugs form a covalent bond with their target proteinthis concept is underexplored in the field of RNA drugging [37]. Recent studies suggest that the validation of RNA-small molecule interactions [38][39][40], drug efficacy or the identification of off-target effects of approved drugs on the transcriptome [41,42] could greatly benefit from covalency. We believe that these exciting new research directions will be furthered by the efficient synthetic routes to covalent RNA binders presented here. ...
The preQ 1 cIass-I riboswitch aptamer can utilize 7-aminomethyl-7-deazaguanine (preQ 1 ) 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 preQ 1 and (2,6-diamino-7-aminomethyl-7-deazapurine) DPQ 1 ligands. The readily accessible key intermediates of preQ 0 and DPQ 0 (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 preQ 0 and DPQ 0 that allow for cleaner and faster access to preQ 1 compared to existing routes and provide the novel compound DPQ 1 .
... Since the early days of DNA crystallography (2), a variety of experimental results have indicated that the DNA threedimensional shape and structural flexibility depend on the sequence of bases (3)(4)(5)(6)(7), and analogous experimental data have been accumulating also for the RNA double helix (8)(9)(10)(11)(12). Sequence-dependent shape and stiffness of the NA double helices affect their interaction with proteins (13,14) and small ligands, some of them potential therapeutic agents (15)(16)(17)(18). The knowledge of sequence-specific structural information greatly improves the performance of algorithms aimed at predicting transcription factor affinities (19)(20)(21)(22)(23)(24) or identifying promoter regions (25,26). ...
The structure and deformability of double-stranded DNA and RNA depend on the sequence of bases, affecting biological processes and nanostructure design. Despite intense research, the dependence is incompletely understood. Here we present mechanical properties of DNA and RNA duplexes inferred from atomic-resolution, explicit-solvent molecular dynamics (MD) simulations of 107 DNA and 107 RNA oligomers containing all hexanucleotide sequences. The sequence-specific parameters include structure and stiffness at the rigid base level, the width and stiffness of major and minor grooves, and global material constants such as stretch modulus, twist rigidity, or bending and twisting persistence lengths. We propose a simple model to predict sequence-dependent shape and harmonic stiffness for arbitrary sequence, validate it on an independent set of MD simulations for DNA and RNA duplexes containing all pentamers, and demonstrate its utility in various applications. The large amount of simulated data enabled us to study rare events, such as base-pair opening lifetimes, or flips of the RNA sugar pucker into the B domain and the related dynamics of the ribose OH group. Together, this work provides a comprehensive sequence-specific description of DNA and RNA duplex mechanics, forming a baseline for further research and allowing for a broad range of applications.
Iron responsive element (IREs) mRNA and iron regulatory proteins (IRPs) regulate iron homeostasis. 5′-untranslated region motifs of APP IREs fold into RNA stem loops bind to IRP to control translation. Through the 5’-UTR APP IREs, iron overload accelerated the translation of the Alzheimer’s amyloid precursor protein (APP). The protein synthesis activator eIF4F and the protein synthesis repressor IRP1 are the two types of proteins that IREs bind. Iron regulates the competitive binding of eIF4F and IRP1 to IRE. Iron causes the IRE and eIF4F to associate with one other, causing the dissociation of IRPs and altered translation. In order to control IRE-modulated expression of APP, messenger RNAs are becoming attractive targets for the development of small molecule therapeutics. Many mRNA interference strategies target the 2-D RNA structure, but messenger RNAs like rRNAs and tRNAs can fold into complicated, three-dimensional structures that add another level of complexity. IREs family is one of the few known 3-D mRNA regulatory elements. In this review, I present IREs structural and functional characteristics. For iron metabolism, the mRNAs encoding the proteins are controlled by this family of similar base sequences. Iron has a similar way of controlling the expression of Alzheimer’s APP as ferritin IRE RNA in their 5ÚTR. Further, iron mis regulation by IRPs can be investigated and contrasted using measurements of expression levels of APP, amyloid-β and tau formation. Accordingly, IRE-modulated APP expression in Alzheimer’s disease has great therapeutic potential through targeting mRNA structures.
Functional RNA molecules are crucial for biological processes from gene regulation to protein synthesis, and analyzing functional motifs and elements is essential for understanding RNA regulation. Building on RegRNA 1.0 and 2.0, we present RegRNA 3.0, a sophisticated meta-workflow that integrates 26 computational tools and 28 databases for annotation, enabling one-step and customizable RNA motif predictions. RegRNA streamlines multi-step analysis and enhances result interpretation with interactive visualizations and comprehensive reporting tools. When provided with an RNA sequence, RegRNA 3.0 generates predictions for RNA functional motifs, RNA interaction motifs, and comprehensive RNA annotations. Specifically, RNA functional motifs include core promoter elements, RNA decay, G-quadruplex, and 14 previous types. RNA interaction motifs include newly added RNA–ligand interactions and RNA–binding protein predictions, along with three previous types. RNA annotation includes RNA family classification, blood exosomes RNA, subcellular localizations, A-to-I editing events, modifications, and 3D structures, along with four previously supported features. RegRNA 3.0 accelerates gene regulation and RNA biology discoveries by offering a user-friendly platform for identifying and analyzing RNA motifs and interactions. The web interface has been improved for intuitive visualizations of predicted motifs and structures, with flexible download options in multiple formats. It is available at http://awi.cuhk.edu.cn/∼RegRNA/.
Tauopathies are neurodegenerative diseases characterized by the abnormal accumulation of microtubule-associated protein tau (MAPT) in the brain. These disorders, like frontotemporal dementia (FTD-Tau), currently lack effective therapies and can occur sporadically or be inherited when associated with MAPT gene mutations. The MAPT gene region encompassing exon 10 and adjacent introns is a hotspot for pathogenic variants, including splicing mutations that enhance exon 10 inclusion and increase 4R tau expression, and gain-of-function mutations that generate aggregation-prone mutant 4R tau protein. For these 4R-specific tauopathies, a targeted mRNA splicing approach that promotes exon 10 exclusion may offer therapeutic benefit. In this study, we discovered novel splicing modulator compounds (SMCs) that promote MAPT exon 10 exclusion, and demonstrated their efficacy in FTD patient-derived neuronal models carrying the tau-P301L gain-of-function mutation or the tau-S305N splicing mutation. Treatment with SMC reduced 4R tau expression and decreased the accumulation of hyperphosphorylated tau (pTau), oligomeric and insoluble tau, thereby rescuing tau-associated neuronal toxicity. Importantly, our lead SMC corrected the 3R/4R splice ratio in vivo and significantly reduced pTau in the brain of a gene- replacement (GR) mouse model expressing the human tau-N279K splicing mutation. These findings support the therapeutic potential of this class of small molecules and establish MAPT pre- mRNA splicing modulation as a promising strategy for the treatment of 4R tauopathies.
One Sentence Summary
Discovery of SMCs that correct MAPT splicing, reduce 4R tau, and rescue pathology in patient- derived neuronal and in vivo models of 4R tauopathies.
Despite the promise of vastly expanding the druggable genome, rational design of RNA-targeting ligands remains challenging as it requires the rapid identification of hits and visualization of the resulting cocomplexes for guiding optimization. Here, we leveraged high-throughput screening, medicinal chemistry, and structural biology to identify a de novo splicing inhibitor against a large and highly folded fungal group I intron. High-resolution cryoEM structures of the intron in different liganded states not only reveal molecular interactions that rationalize experimental structure–activity relationship but also shed light on a unique strategy whereby RNA-associated metal ions and RNA conformation exhibit exceptional plasticity in response to small-molecule binding. This study reveals general principles that govern RNA–ligand recognition, the interplay between chemical bonding specificity, and dynamic responses within an RNA target.
Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and is responsible for about 467,000 cancer deaths annually. An oftentimes asymptomatic early phase of this disease results in a delayed diagnosis, and patients often present with advanced disease. Current treatment options have limited survival benefits, and only a minor patient population carries actionable genomic alterations. Hence, innovative personalized treatment strategies that consider molecular, cellular and functional analyses are urgently needed for pancreatic cancer patients. However, the majority of the genetic alterations found in PDAC are currently undruggable, or patients’ response is not as expected. Therefore, non-genomic biomarkers and alternative molecular targets should be considered in order to advance the clinical management of PDAC patients. In line with this, recent gene expression and single-cell transcriptome analyses have identified molecular subtypes and transcriptional cell states that affect disease progression and drug efficiency. In this review, we will introduce long non-coding RNAs (lncRNAs) as well as RNA-binding proteins (RBPs) that are able to modulate the transcriptome of a cell through diverse mechanisms, thereby contributing to disease progression. We will provide a brief overview about the general functions of lncRNAs and RBPs, respectively. Subsequently, we will highlight selected lncRNAs and RBPs that have been shown to play a role in PDAC development, progression and drug response. Finally, we will present strategies aiming to interfere with the expression and function of lncRNAs and RBPs.
Ligand–receptor docking simulation is difficult when the biomolecules have high intrinsic flexibility. If some knowledge on the ligand–receptor complex structure or inter-molecular contact sites are presented in advance, the difficulty of docking problem considerably decreases. This paper proposes a generalized-ensemble method “cartesian-space division mD-VcMD” (or CSD-mD-VcMD), which calculates stable complex structures without assist of experimental knowledge on the complex structure. This method is an extension of our previous method that requires the knowledge on the ligand–receptor complex structure in advance. Both the present and previous methods enhance the conformational sampling, and finally produce a binding free-energy landscape starting from a completely dissociated conformation, and provide a free-energy landscape. We applied the present method to same system studied by the previous method: A ligand (ribocil A or ribocil B) binding to an RNA (the aptamer domain of the FMN riboswitch). The two methods produced similar results, which explained experimental data. For instance, ribocil B bound to the aptamer’s deep binding pocket more strongly than ribocil A did. However, this does not mean that two methods have a similar performance. Note that the present method did not use the experimental knowledge of binding sites although the previous method was supported by the knowledge. The RNA-ligand binding site could be a cryptic site because RNA and ligand are highly flexible in general. The current study showed that CSD-mD-VcMD is actually useful to obtain a binding free-energy landscape of a flexible system, i.e., the RNA-ligand interacting system.
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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.
Small interfering RNA (siRNA) has been deemed a promising therapeutic method for treating diverse diseases. siRNA-based therapeutics provide a distinct mechanism of action by selectively targeting and silencing disease-causing genes at the post-transcriptional level. This paper provides an overview of the present state of siRNA-based therapeutics, highlighting their potential in different therapeutic areas. The first section of this review introduces the basic principles of siRNA technology, including its mechanism of action and delivery methods. Subsequently, we discuss the impediments associated with siRNA delivery and manufacturing development and the strategies for overcoming these obstacles. The clinical advancement of siRNA therapeutics in various disease areas, including cancer, genetic disorders, viral infections, and inflammatory diseases, is summarized. Lastly, we summarize the successes, failures, and lessons learned from the development of siRNAs. With advancements in delivery systems and improvements in target selection, the field of medicine can be revolutionized, and siRNA therapeutics can offer new treatment options for patients.
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.
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.
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.
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.
The fourth Industrial Revolution facilitates a symbiotic relationship between computational techniques and material development, with special emphasis in the domain of bioinspired materials. This initiative aims to propel interdisciplinary research by integrating computational technology with biomaterials, expediting advancements in fabrication and design. Computational design and simulations also offer an expansive landscape to engineer the next‐generation biomaterials utilizing nuclei‐acid based materials, spanning from the molecular to macroscopic levels, guided by specific molecular dynamics principles. This review aims to provide a succinct overview of prevailing computational techniques and multiscale simulations utilized in design of ribonucleic acid (RNA)‐based nanomaterials. By elucidating the interplay between structure and function, computational approaches facilitate the creation of biomimetic design and structures with tailored properties and functionalities for diverse applications. It underscores interdisciplinary collaborations, wherein insights from natural biomaterials inspire the rational design and synthesis of novel hierarchical structures using computational methodologies. Through a systematic exploration of current research paradigms, this review endeavors to delineate pathways for future innovation and advancement in the field of RNA‐based materials, fostering transformative impacts across sectors such as healthcare, biotechnology, and beyond.
Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA that target specific ligands face limitations in capturing RNA's conformational flexibility, ensuring structural validity, and overcoming data scarcity. To address these challenges, we introduce RiboFlow, a synergistic flow matching model to co-design RNA structures and sequences based on target molecules. By integrating RNA backbone frames, torsion angles, and sequence features in an unified architecture, RiboFlow explicitly models RNA's dynamic conformations while enforcing sequence-structure consistency to improve validity. Additionally, we curate RiboBind, a large-scale dataset of RNA-molecule interactions, to resolve the scarcity of high-quality structural data. Extensive experiments reveal that RiboFlow not only outperforms state-of-the-art RNA design methods by a large margin but also showcases controllable capabilities for achieving high binding affinity to target ligands. Our work bridges critical gaps in controllable RNA design, offering a framework for structure-aware, data-efficient generation.
RNA structures are essential for understanding their biological functions and developing RNA-targeted therapeutics. However, accurate RNA structure prediction from sequence remains a crucial challenge. We introduce DRfold2, a deep learning framework that integrates a novel pre-trained RNA Composite Language Model (RCLM) with a denoising structure module for end-to-end RNA structure prediction. DRfold2 achieves superior performance in both global topology and secondary structure predictions over other state-of-the-art approaches across multiple benchmark tests. Detailed analyses reveal that the improvements primarily stem from the RCLM’s ability to capture co-evolutionary pattern and the effective denoising process, leading to a more than 100% increase in contact prediction precision compared to existing methods. Furthermore, DRfold2 demonstrates high complementarity with AlphaFold3, achieving statistically significant accuracy gains when integrated into our optimization framework. By uniquely combining composite language modeling, denoise-based end-to-end learning, and deep learning-guided post-optimization, DRfold2 establishes a distinct direction for advancing ab initio RNA structure prediction.
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.
Huntington’s Disease (HD) is a progressive neurodegenerative disorder caused by CAG trinucleotide repeat expansions in exon 1 of the huntingtin ( HTT ) gene. The mutant HTT (mHTT) protein causes neuronal dysfunction, causing progressive motor, cognitive and behavioral abnormalities. Current treatments for HD only alleviate symptoms, but cerebral spinal fluid (CSF) or central nervous system (CNS) delivery of antisense oligonucleotides (ASOs) or virus vectors expressing RNA-induced silencing (RNAi) moieties designed to induce mHTT mRNA lowering have progressed to clinical trials. Here, we present an alternative disease modifying therapy the orally available, brain penetrant small molecule branaplam. By promoting inclusion of a pseudoexon in the primary transcript, branaplam lowers mHTT protein levels in HD patient cells, in an HD mouse model and in blood samples from Spinal Muscular Atrophy (SMA) Type I patients dosed orally for SMA (NCT02268552). Our work paves the way for evaluating branaplam’s utility as an HD therapy, leveraging small molecule splicing modulators to reduce expression of dominant disease genes by driving pseudoexon inclusion.
Significance
Drug discovery generally investigates one target at a time, in sharp contrast to living organisms, which mold ligands and targets by evolution of highly complex molecular interaction networks. We recapitulate this modality of discovery by encoding drug structures in DNA, allowing the entire DNA-encoded library to interact with thousands of RNA fold targets, and then decoding both drug and target by sequencing. This information serves as a filter to identify human RNAs aberrantly produced in cancer that are also binding partners of the discovered ligand, leading to a precision medicine candidate that selectively ablates an oncogenic noncoding RNA, reversing a disease-associated phenotype in cells.
Fungal pathogens represent an expanding global health threat for which treatment options are limited. Self-splicing group II introns have emerged as promising drug targets, but their development has been limited by a lack of information on their distribution and architecture in pathogenic fungi. To meet this challenge, we developed a bioinformatic workflow for scanning sequence data to identify unique RNA structural signatures within group II introns. Using this approach, we discovered a set of ubiquitous introns within thermally dimorphic fungi (genera of Blastomyces, Coccidioides and Histoplasma). These introns are the most biochemically reactive group II introns ever reported, and they self-splice rapidly under near-physiological conditions without protein cofactors. Moreover, we demonstrated the small molecule targetability of these introns by showing that they can be inhibited by the FDA-approved drug mitoxantrone in vitro. Taken together, our results highlight the utility of structure-based informatic searches for identifying riboregulatory elements in pathogens, revealing a striking diversity of reactive self-splicing introns with great promise as antifungal drug targets.
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.
The role of metabolite-responsive riboswitches in regulating gene expression in bacteria is well known and makes them useful systems for the study of RNA-small molecule interactions. Here, we study the PreQ1 riboswitch system, assessing sixteen diverse PreQ1-derived probes for their ability to selectively modify the class-I PreQ1 riboswitch aptamer covalently. For the most active probe (11), a diazirine-based photocrosslinking analog of PreQ1, X-ray crystallography and gel-based competition assays demonstrated the mode of binding of the ligand to the aptamer, and functional assays demonstrated that the probe retains activity against the full riboswitch. Transcriptome-wide mapping using Chem-CLIP revealed a highly selective interaction between the bacterial aptamer and the probe. In addition, a small number of RNA targets in endogenous human transcripts were found to bind specifically to 11, providing evidence for candidate PreQ1 aptamers in human RNA. This work demonstrates a stark influence of linker chemistry and structure on the ability of molecules to crosslink RNA, reveals that the PreQ1 aptamer/ligand pair are broadly useful for chemical biology applications, and provides insights into how PreQ1, which is similar in structure to guanine, interacts with human RNAs.
Iron responsive elements (IREs) are mRNA stem-loop targets for translational control by the two iron regulatory proteins IRP1 and IRP2. They are found in the untranslated regions (UTRs) of genes that code for proteins involved in iron metabolism. There are ten “classic” IRE types that define the conserved secondary and tertiary structure elements necessary for proper IRP binding, and there are 83 published “IRE-like” sequences, most of which depart from the established IRE model. Here are structurally-guided discussions regarding the essential features of an IRE and what is important for IRE family membership.
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 ¹H‐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.
The RNA genome of SARS-CoV-2 contains 15 independently folded regulatory RNA elements. By NMR-based fragment screening, the RNA target space for small molecule binding, functional mapping and inhibition is defined.
Abstract
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 ¹H-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.
Due to the mounting evidence that RNA structure plays a critical role in regulating almost any physiological as well as pathological process, being able to accurately define the folding of RNA molecules within living cells has become a crucial need. We introduce here 2-aminopyridine-3-carboxylic acid imidazolide (2A3), as a general probe for the interrogation of RNA structures in vivo. 2A3 shows moderate improvements with respect to the state-of-the-art selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reagent NAI on naked RNA under in vitro conditions, but it significantly outperforms NAI when probing RNA structure in vivo, particularly in bacteria, underlining its increased ability to permeate biological membranes. When used as a restraint to drive RNA structure prediction, data derived by SHAPE-MaP with 2A3 yields more accurate predictions than NAI-derived data. Due to its extreme efficiency and accuracy, we can anticipate that 2A3 will rapidly take over conventional SHAPE reagents for probing RNA structures both in vitro and in vivo.
Significance
The development of selective bioactive compounds that target RNA structures is difficult. Here we report an approach to identify low molecular weight fragments that bind to a cancer-causing RNA. By determining where the fragments bind within an RNA target, they can be assembled to provide a high-affinity and potent inhibitor. This approach could be a general way to develop bioactive small molecules that target RNA, which previously was considered “undruggable.” It was applied to provide a precision lead medicine against an RNA that contributes to cancer metastasis.
Alpha-synuclein (α-Syn) is a 140-amino acid (aa) protein encoded by the Synuclein alpha SNCA gene. It is the synaptic protein associated with Parkinson’s disease (PD) and is the most highly expressed protein in the Lewy bodies associated with PD and other alpha synucleopathies, including Lewy body dementia (LBD) and multiple system atrophy (MSA). Iron deposits are present in the core of Lewy bodies, and there are reports suggesting that divalent metal ions including Cu²⁺ and Fe²⁺ enhance the aggregation of α-Syn. Differential expression of α-Syn is associated with alcohol use disorder (AUD), and specific genetic variants contribute to the risk for alcoholism, including alcohol craving. Spliced variants of α-Syn, leading to the expression of several shorter forms which are more prone to aggregation, are associated with both PD and AUD, and common transcript variants may be able to predict at-risk populations for some movement disorders or subtypes of PD, including secondary Parkinsonism. Both PD and AUD are associated with liver and brain iron dyshomeostasis. Research over the past decade has shown that α-Syn has iron import functions with an ability to oxidize the Fe³⁺ form of iron to Fe²⁺ to facilitate its entry into cells. Our prior research has identified an iron-responsive element (IRE) in the 5’ untranslated region (5’UTR) of α-Syn mRNA, and we have used the α-Syn 5’UTR to screen for small molecules that modulate its expression in the H4 neuronal cell line. These screens have led us to identify several interesting small molecules capable of both decreasing and increasing α-Syn expression and that may have the potential, together with the recently described mesenchymal stem cell therapies, to normalize α-Syn expression in different regions of the alcoholic and PD brain.
Biomolecules form dynamic ensembles of many inter-converting conformations which are key for understanding how they fold and function. However, determining ensembles is challenging because the information required to specify atomic structures for thousands of conformations far exceeds that of experimental measurements. We addressed this data gap and dramatically simplified and accelerated RNA ensemble determination by using structure prediction tools that leverage the growing database of RNA structures to generate a con- formation library. Refinement of this library with NMR residual dipolar couplings provided an atomistic ensemble model for HIV-1 TAR, and the model accuracy was independently sup- ported by comparisons to quantum-mechanical calculations of NMR chemical shifts, com- parison to a crystal structure of a substate, and through designed ensemble redistribution via atomic mutagenesis. Applications to TAR bulge variants and more complex tertiary RNAs support the generality of this approach and the potential to make the determination of atomic-resolution RNA ensembles routine.
Targeting RNAs with small molecules represents a new frontier in drug discovery and development. The rich structural diversity of folded RNAs offers a nearly unlimited reservoir of targets for small molecules to bind, similar to small molecule occupancy of protein binding pockets, thus creating the potential to modulate human biology. Although the bacterial ribosome has historically been the most well exploited RNA target, advances in RNA sequencing technologies and a growing understanding of RNA structure have led to an explosion of interest in the direct targeting of human pathological RNAs. This review highlights recent advances in this area, with a focus on the design of small molecule probes that selectively engage structures within disease-causing RNAs, with micromolar to nanomolar affinity. Additionally, we explore emerging RNA-target strategies, such as bleomycin A5 conjugates and ribonuclease targeting chimeras (RIBOTACs), that allow for the targeted degradation of RNAs with impressive potency and selectivity. The compounds discussed in this review have proven efficacious in human cell lines, patient-derived cells, and pre-clinical animal models, with one compound currently undergoing a Phase II clinical trial and another that recently garnerd FDA-approval, indicating a bright future for targeted small molecule therapeutics that affect RNA function.
Enterovirus 71 (EV71) poses serious threats to human health, particularly in Southeast Asia, and no drugs or vaccines are available. Previous work identified the stem loop II structure of the EV71 internal ribosomal entry site as vital to viral translation and a potential target. After screening an RNA-biased library using a peptide-displacement assay, we identify DMA-135 as a dose-dependent inhibitor of viral translation and replication with no significant toxicity in cell-based studies. Structural, biophysical, and biochemical characterization support an allosteric mechanism in which DMA-135 induces a conformational change in the RNA structure that stabilizes a ternary complex with the AUF1 protein, thus repressing translation. This mechanism is supported by pull-down experiments in cell culture. These detailed studies establish enterovirus RNA structures as promising drug targets while revealing an approach and mechanism of action that should be broadly applicable to functional RNA targeting.
A set of common-acting iron-responsive 5'untranslated region (5'UTR) motifs can fold into RNA stem loops that appear significant to the biology of cognitive declines of Parkinson's disease dementia (PDD), Lewy body dementia (LDD), and Alzheimer's disease (AD). Neurodegenerative diseases exhibit perturbations of iron homeostasis in defined brain subregions over characteristic time intervals of progression. While misfolding of Aβ from the amyloid-precursor-protein (APP), alpha-synuclein, prion protein (PrP) each cause neuropathic protein inclusions in the brain subregions, iron-responsive-like element (IRE-like) RNA stem-loops reside in their transcripts. APP and αsyn have a role in iron transport while gene duplications elevate the expression of their products to cause rare familial cases of AD and PDD. Of note, IRE-like sequences are responsive to excesses of brain iron in a potential feedback loop to accelerate neuronal ferroptosis and cognitive declines as well as amyloidosis. This pathogenic feedback is consistent with the translational control of the iron storage protein ferritin. We discuss how the IRE-like RNA motifs in the 5'UTRs of APP, alpha-synuclein and PrP mRNAs represent uniquely folded drug targets for therapies to prevent perturbed iron homeostasis that accelerates AD, PD, PD dementia (PDD) and Lewy body dementia, thus preventing cognitive deficits. Inhibition of alpha-synuclein translation is an option to block manganese toxicity associated with early childhood cognitive problems and manganism while Pb toxicity is epigenetically associated with attention deficit and later-stage AD. Pathologies of heavy metal toxicity centered on an embargo of iron export may be treated with activators of APP and ferritin and inhibitors of alpha-synuclein translation.
The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. Here, we demonstrate that cryo-electron microscopy can routinely resolve maps of RNA-only systems and that these maps enable subnanometer-resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. This hybrid ‘Ribosolve’ pipeline detects and falsifies homologies and conformational rearrangements in 11 previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length Vibrio cholerae and Fusobacterium nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and the computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.
Comparative sequence analyses have been used to discover numerous classes of structured noncoding RNAs, some of which are riboswitches that specifically recognize small-molecule or elemental ion ligands and influence expression of adjacent downstream genes. Determining the correct identity of the ligand for a riboswitch candidate typically is aided by an understanding of the genes under its regulatory control. Riboswitches whose ligands were straightforward to identify have largely been associated with well-characterized metabolic pathways, such as coenzyme or amino acid biosynthesis. Riboswitch candidates whose ligands resist identification, collectively known as orphan riboswitches, are often associated with genes coding for proteins of unknown function, or genes for various proteins with no established link to one another. The cognate ligands for 16 former orphan riboswitch motifs have been identified to date. The successful pursuit of the ligands for these classes has provided insight into areas of biology that are not yet fully explored, such as ion homeostasis, signaling networks, and other previously underappreciated biochemical or physiological processes. Herein we discuss the strategies and methods used to match ligands with orphan riboswitch classes, and overview the lessons learned to inform and motivate ongoing efforts to identify ligands for the many remaining candidates.
Low-abundance short-lived non-native conformations referred to as excited states (ESs) are increasingly observed in vitro and implicated in the folding and biological activities of regulatory RNAs. We developed an approach for assessing the relative abundance of RNA ESs within the functional cellular context. Nuclear magnetic resonance (NMR) spectroscopy was used to estimate the degree to which substitution mutations bias conformational equilibria toward the inactive ES in vitro. The cellular activity of the ES-stabilizing mutants was used as an indirect measure of the conformational equilibria within the functional cellular context. Compensatory mutations that restore the ground-state conformation were used to control for changes in sequence. Using this approach, we show that the ESs of two regulatory RNAs from HIV-1, the transactivation response element (TAR) and the Rev response element (RRE), likely form in cells with abundances comparable to those measured in vitro, and their targeted stabilization may provide an avenue for developing anti-HIV therapeutics.
Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long noncoding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures.
Supplementary information:
Supplementary data are available at Bioinformatics online and from the R-scape web server eddylab.org/R-scape, with a link to download the source code.
As the area of small molecules interacting with RNA advances, general routes to provide bioactive compounds are needed as ligands can bind RNA avidly to sites that will not affect function. Small-molecule targeted RNA degradation will thus provide a general route to affect RNA biology. A non–oligonucleotide-containing compound was designed from sequence to target the precursor to oncogenic microRNA-21 (pre–miR-21) for enzymatic destruction with selectivity that can exceed that for protein-targeted medicines. The compound specifically binds the target and contains a heterocycle that recruits and activates a ribonuclease to pre–miR-21 to substoichiometrically effect its cleavage and subsequently impede metastasis of breast cancer to lung in a mouse model. Transcriptomic and proteomic analyses demonstrate that the compound is potent and selective, specifically modulating oncogenic pathways. Thus, small molecules can be designed from sequence to have all of the functional repertoire of oligonucleotides, including inducing enzymatic degradation, and to selectively and potently modulate RNA function in vivo.
Many proteins are refractory to targeting because they lack small-molecule binding pockets. An alternative to drugging these proteins directly is to target the messenger (m)RNA that encodes them, thereby reducing protein levels. We describe such an approach for the difficult-to-target protein α-synuclein encoded by the SNCA gene. Multiplication of the SNCA gene locus causes dominantly inherited Parkinson’s disease (PD), and α-synuclein protein aggregates in Lewy bodies and Lewy neurites in sporadic PD. Thus, reducing the expression of α-synuclein protein is expected to have therapeutic value. Fortuitously, the SNCA mRNA has a structured iron-responsive element (IRE) in its 5′ untranslated region (5′ UTR) that controls its translation. Using sequence-based design, we discovered small molecules that target the IRE structure and inhibit SNCA translation in cells, the most potent of which is named Synucleozid. Both in vitro and cellular profiling studies showed Synucleozid directly targets the α-synuclein mRNA 5′ UTR at the designed site. Mechanistic studies revealed that Synucleozid reduces α-synuclein protein levels by decreasing the amount of SNCA mRNA loaded into polysomes, mechanistically providing a cytoprotective effect in cells. Proteome- and transcriptome-wide studies showed that the compound’s selectivity makes Synucleozid suitable for further development. Importantly, transcriptome-wide analysis of mRNAs that encode intrinsically disordered proteins revealed that each has structured regions that could be targeted with small molecules. These findings demonstrate the potential for targeting undruggable proteins at the level of their coding mRNAs. This approach, as applied to SNCA , is a promising disease-modifying therapeutic strategy for PD and other α-synucleinopathies.
Splice-switching antisense oligonucleotides (ASOs), which bind specific RNA-target sequences and modulate pre-mRNA splicing by sterically blocking the binding of splicing factors to the pre-mRNA, are a promising therapeutic modality to treat a range of genetic diseases. ASOs are typically 15-25 nt long and considered to be highly specific towards their intended target sequence, typically elements that control exon definition and/or splice-site recognition. However, whether or not splice-modulating ASOs also induce hybridization-dependent mis-splicing of unintended targets has not been systematically studied. Here, we tested the in vitro effects of splice-modulating ASOs on 108 potential off-targets predicted on the basis of sequence complementarity, and identified 17 mis-splicing events for one of the ASOs tested. Based on analysis of data from two overlapping ASO sequences, we conclude that off-target effects are difficult to predict, and the choice of ASO chemistry influences the extent of off-target activity. The off-target events caused by the uniformly modified ASOs tested in this study were significantly reduced with mixed-chemistry ASOs of the same sequence. Furthermore, using shorter ASOs, combining two ASOs, and delivering ASOs by free uptake also reduced off-target activity. Finally, ASOs with strategically placed mismatches can be used to reduce unwanted off-target splicing events.
Specimens below 50 kDa have generally been considered too small to be analyzed by single-particle cryo-electron microscopy (cryo-EM). The high flexibility of pure RNAs makes it difficult to obtain high-resolution structures by cryo-EM. In bacteria, riboswitches regulate sulfur metabolism through binding to the S-adenosylmethionine (SAM) ligand and offer compelling targets for new antibiotics. SAM-I, SAM-I/IV, and SAM-IV are the three most commonly found SAM riboswitches, but the structure of SAM-IV is still unknown. Here, we report the structures of apo and SAM-bound SAM-IV riboswitches (119-nt, ~40 kDa) to 3.7 Å and 4.1 Å resolution, respectively, using cryo-EM. The structures illustrate homologies in the ligand-binding core but distinct peripheral tertiary contacts in SAM-IV compared to SAM-I and SAM-I/IV. Our results demonstrate the feasibility of resolving small RNAs with enough detail to enable detection of their ligand-binding pockets and suggest that cryo-EM could play a role in structure-assisted drug design for RNA.
The most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (c9ALS/FTD) is an expanded G4C2 RNA repeat [r(G4C2)exp] in chromosome 9 open reading frame 72 (C9orf72), which elicits pathology through several mechanisms. Here, we developed and characterized a small molecule for targeted degradation of r(G4C2)exp. The compound was able to selectively bind r(G4C2)exp’s structure and to assemble an endogenous nuclease onto the target, provoking removal of the transcript by native RNA quality control mechanisms. In c9ALS patient–derived spinal neurons, the compound selectively degraded the mutant C9orf72 allele with limited off-targets and reduced quantities of toxic dipeptide repeat proteins (DPRs) translated from r(G4C2)exp. In vivo work in a rodent model showed that abundance of both the mutant allele harboring the repeat expansion and DPRs were selectively reduced by this compound. These results demonstrate that targeted small-molecule degradation of r(G4C2)exp is a strategy for mitigating c9ALS/FTD-associated pathologies and studying disease-associated pathways in preclinical models.
Machine learning solves RNA puzzles
RNA molecules fold into complex three-dimensional shapes that are difficult to determine experimentally or predict computationally. Understanding these structures may aid in the discovery of drugs for currently untreatable diseases. Townshend et al . introduced a machine-learning method that significantly improves prediction of RNA structures (see the Perspective by Weeks). Most other recent advances in deep learning have required a tremendous amount of data for training. The fact that this method succeeds given very little training data suggests that related methods could address unsolved problems in many fields where data are scarce. —DJ
Flavaglines such as silvestrol (1) and rocaglamide (2) constitute an interesting class of natural products with promising anticancer activities. Their mode of action is based on inhibition of eukaryotic initiation factor 4A (eIF4A) dependent translation through formation of a stable ternary complex with eIF4A and mRNA, thus blocking ribosome scanning. Herein we describe initial SAR studies in a novel series of 1-aminomethyl substituted flavagline-inspired eIF4A inhibitors. We discovered that a variety of N-substitutions at the 1-aminomethyl group are tolerated, making this position pertinent for property and ADME profile tuning. The findings presented herein are relevant to future drug design efforts towards novel eIF4A inhibitors with drug-like properties.
To maintain an adequate iron supply for hemoglobin synthesis and essential metabolic functions while counteracting iron toxicity, humans and other vertebrates have evolved effective mechanisms to conserve and finely regulate iron concentration, storage, and distribution to tissues. At the systemic level, the iron-regulatory hormone hepcidin is secreted by the liver in response to serum iron levels and inflammation. Hepcidin regulates the expression of the sole known mammalian iron exporter, ferroportin, to control dietary absorption, storage and tissue distribution of iron. At the cellular level, iron regulatory proteins 1 and 2 (IRP1 and IRP2) register cytosolic iron concentrations and post-transcriptionally regulate the expression of iron metabolism genes to optimize iron availability for essential cellular processes, including heme biosynthesis and iron-sulfur cluster biogenesis. Genetic malfunctions affecting the iron sensing mechanisms or the main pathways that utilize iron in the cell cause a broad range of human diseases, some of which are characterized by mitochondrial iron accumulation. This review will discuss the mechanisms of systemic and cellular iron sensing with a focus on the main iron utilization pathways in the cell, and on human conditions that arise from compromised function of the regulatory axes that control iron homeostasis.
Nucleic acids do not fold into a single conformation, and dynamic ensembles are needed to describe their propensities to cycle between different conformations when performing cellular functions. We review recent advances in solution-state nuclear magnetic resonance (NMR) methods and their integration with computational techniques that are improving the ability to probe the dynamic ensembles of DNA and RNA. These include computational approaches for predicting chemical shifts from structure and generating conformational libraries from sequence, measurements of exact nuclear Overhauser effects, development of new probes to study chemical exchange using relaxation dispersion, faster and more sensitive real-time NMR techniques, and new NMR approaches to tackle large nucleic acid assemblies. We discuss how these advances are leading to new mechanistic insights into gene expression and regulation.
Nature has three biopolymers: oligonucleotides, polypeptides, and oligosaccharides. Each biopolymer has independent functions, but when needed, they form mixed assemblies for higher-order purposes, as in the case of ribosomal protein synthesis. Rather than forming large complexes to coordinate the role of different biopolymers, we dovetail protein amino acids and nucleobases into a single low molecular weight precision polyamide polymer. We established efficient chemical synthesis and de novo sequencing procedures and prepared combinatorial libraries with up to 100 million biohybrid molecules. This biohybrid material has a higher bulk affinity to oligonucleotides than peptides composed exclusively of canonical amino acids. Using affinity selection mass spectrometry, we discovered variants with a high affinity for pre-microRNA hairpins. Our platform points toward the development of high throughput discovery of sequence defined polymers with designer properties, such as oligonucleotide binding.
Many diseases are caused by toxic RNA repeats. Herein, we designed a lead small molecule that binds the structure of the r(CUG) repeat expansion [r(CUG) exp] that causes myotonic dystrophy type 1 (DM1) and Fuchs endothelial corneal dystrophy (FECD) and rescues disease biology in patient-derived cells and in vivo. Interestingly, the compound's downstream effects are different in the two diseases, owing to the location of the repeat expansion. In DM1, r(CUG) exp is harbored in the 3′ untranslated region, and the compound has no effect on the mRNA's abundance. In FECD, however, r(CUG) exp is located in an intron, and the small molecule facilitates excision of the intron, which is then degraded by the RNA exosome complex. Thus, structure-specific, RNA-targeting small molecules can act disease specifically to affect biology, either by disabling the gain-of-function mechanism (DM1) or by stimulating quality control pathways to rid a disease-affected cell of a toxic RNA (FECD).
Riboswitches are mRNA domains that make gene-regulatory decisions upon binding their cognate ligands. Bacterial riboswitches that specifically recognize 5-aminoimidazole-4-carboxamide riboside 5′-monophosphate (ZMP) and 5′-triphosphate (ZTP) regulate genes involved in folate and purine metabolism. Now, we have developed synthetic ligands targeting ZTP riboswitches by replacing the sugar-phosphate moiety of ZMP with various functional groups, including simple heterocycles. Despite losing hydrogen bonds from ZMP, these analogs bind ZTP riboswitches with similar affinities as the natural ligand, and activate transcription more strongly than ZMP in vitro. The most active ligand stimulates gene expression ∼3 times more than ZMP in a live Escherichia coli reporter. Co-crystal structures of the Fusobacterium ulcerans ZTP riboswitch bound to synthetic ligands suggest stacking of their pyridine moieties on a conserved RNA nucleobase primarily determines their higher activity. Altogether, these findings guide future design of improved riboswitch activators and yield insights into how RNA-targeted ligand discovery may proceed.
Rocaglates, rocaglamides, and related flavagline natural products exert their remarkable anticancer activity through inhibition of eukaryotic initiation factor 4A (eIF4A) but generally display suboptimal drug-like properties. In our efforts to identify potent drug-like eIF4A inhibitors, we developed synthetic strategies for diastereoselectively functionalizing the C1 position of aza-rocaglamide scaffolds (cf. 14 and 18), which proceed via retention or inversion of configuration at C1 depending on the C2 substituent (cf. 15 and 21) and ultimately enabled the discovery of novel and potent eIF4A inhibitors such as 25.
Covering: up to the beginning of 2020
Many natural substances have been transformed again and again with regard to their pharmaceutical-medical potential, including new members of a growing class of natural products, the flavaglines. Important representatives are rocaglamide and silvestrol, isolated from the Aglaia species, which are highlighted here. These products started as potential anti-tumor agents five decades ago and have recently proved to be very promising antiviral agents, especially against RNA viruses. Today they are discussed as potential starting compounds for developing drug candidates and therapeutics.
Selectivity is a key requirement of high-quality chemical probes and lead medicines, however methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.
Expanded RNA repeats cause more than 30 incurable diseases. One approach to mitigate their toxicity is by using small molecules that assemble into potent, oligomeric species upon binding to the disease-causing RNA in cells. Herein, we show that the expanded repeat [r(CUG)exp] that causes myotonic dystrophy type 1 (DM1) catalyzes the in situ synthesis of its own inhibitor using an RNA-templated tetrazine ligation in DM1 patient-derived cells. The compound synthesized on-site improved DM1-associated defects at picomolar concentrations, enhancing potency by 10 000-fold, compared to its parent compounds that cannot undergo oligomerization. A fluorogenic reaction is also described where r(CUG)exp templates the synthesis of its own imaging probe to enable visualization of the repeat in its native context in live cells and muscle tissue.
Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.
Dysregulation of protein translation is a key driver for the pathogenesis of many cancers. Eukaryotic initiation factor 4A (eIF4A), an ATP-dependent DEAD-box RNA helicase, is a critical component of the eIF4F complex, which regulates cap-dependent protein synthesis. The flavagline class of natural products (i.e., rocaglamide A) has been shown to inhibit protein synthesis by stabilizing a translation-incompetent complex for select messenger RNAs (mRNAs) with eIF4A. Despite showing promising anticancer phenotypes, the development of flavagline derivatives as therapeutic agents has been hampered because of poor drug-like properties as well as synthetic complexity. A focused effort was undertaken utilizing a ligand-based design strategy to identify a chemotype with optimized physicochemical properties. Also, detailed mechanistic studies were undertaken to further elucidate mRNA sequence selectivity, key regulated target genes, and the associated antitumor phenotype. This work led to the design of eFT226 (Zotatifin), a compound with excellent physicochemical properties and significant antitumor activity that supports clinical development.
Approximately 95% of human genes are alternatively spliced, and aberrant splicing events can cause disease. One pre-mRNA that is alternatively spliced and linked to neurodegenerative diseases is tau (microtubule-associated protein tau), which can cause frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17) and can contribute to Alzheimer's disease. Here, we describe the design of structure-specific lead small molecules that directly target tau pre-mRNA from sequence. This was followed by hit expansion and analogue synthesis to further improve upon these initial lead molecules. The emergent compounds were assessed for functional activity in a battery of assays, including binding assays and an assay that mimics molecular recognition of tau pre-mRNA by a U1 small nuclear ribonucleoprotein (snRNP) splicing factor. Compounds that emerged from these studies had enhanced potency and selectivity for the target RNA relative to the initial hits, while also having significantly improved drug-like properties. The compounds are shown to directly target tau pre-mRNA in cells, via chemical cross-linking and isolation by pull-down target profiling, and to rescue disease-relevant splicing of tau pre-mRNA in a variety of cellular systems, including primary neurons. More broadly, this study shows that lead, structure-specific compounds can be designed from sequence and then further optimized for their physicochemical properties while at the same time enhancing their activity.
Many RNAs are processed into biologically active transcripts, the aberrant expression of which can contribute to disease phenotypes. For example, the primary microRNA-17-92 (pri-miR-17-92) cluster contains six microRNAs (miRNAs) that collectively act in several disease settings. Herein, we used sequence-based design of structure-specific ligands to target a common structure in the Dicer processing sites of three miRNAs in the cluster, miR-17, miR-18a, and miR-20a, thereby inhibiting their biogenesis. The compound was optimized to afford a dimeric molecule that binds the Dicer processing site and an adjacent bulge, affording a 100-fold increase in potency. The dimer's mode of action was then extended from simple binding to direct cleavage by conjugation to bleomycin A5 in a manner that imparts RNA-selective cleavage or to indirect cleavage by recruiting an endogenous nuclease, or a ribonuclease targeting chimera (RIBOTAC). Interestingly, the dimer-bleomycin conjugate cleaves the entire pri-miR-17-92 cluster and hence functionally inhibits all six miRNAs emanating from it. The compound selectively reduced levels of the cluster in three disease models: polycystic kidney disease, prostate cancer, and breast cancer, rescuing disease-associated phenotypes in the latter two. Further, the bleomycin conjugate exerted selective effects on the miRNome and proteome in prostate cancer cells. In contrast, the RIBOTAC only depleted levels of pre- and mature miR-17, -18a, and 20a, with no effect on the primary transcript, in accordance with the cocellular localization of RNase L, the pre-miRNA targets, and the compound. These studies demonstrate a strategy to tune RNA structure-targeting compounds to the cellular localization of the target.
RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.
Ferroptosis, an iron-dependent nonapoptotic cell death, was referred in neurodegenerative diseases, but its role in Parkinson's disease remains unclear. Here, we used ferric ammonium citrate (FAC) to treat dopaminergic cell to mimic the iron overload during the progression of Parkinson's disease (PD). We found that the cell death types of iron-overloaded dopaminergic cells induced by concentrations of FAC were different. Ferroptosis firstly occurred in a relatively low concentration of FAC-treated group, and then apoptosis appeared in response to the increased iron doses. Moreover, both ferroptosis and apoptosis caused by iron overload could be rescued by inhibitors of ferroptosis, but inhibitors of apoptosis did not prevent the occurrence of ferroptosis. We verified that ferroptosis occurred before apoptosis in α-SynA53T homozygous PD mice model. The underlying mechanism might be associated with the p53 signaling pathway, but not MAPK signaling pathway. Collectively, our results revealed a previously unappreciated role of ferroptosis in the early stages of PD and indicated that ferroptosis could elicit apoptosis in cell death caused by iron overload.
Affinity selection (AS)-MS is a label-free binding assay technology for the analysis of interactions between targets and small drug molecules, which does not require modification of targets or compounds. AS-MS technology has been used in drug discovery research for more than 10 years, and is currently one of the most important affinity-based screening techniques. As such, it may be the driving force for novel small molecule drug discovery. This review introduces the principles of AS-MS technology and its use in high-throughput screening (HTS), then discusses strategies for its use in drug discovery and its application in target identification.
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Small-molecule targeted recruitment of nucleases to RNA is a powerful method to affect RNA biology. Inforna, a sequence-based design approach to target RNA, enables the design of small molecules that bind to and cleave RNA in a selective and substoichiometric manner. Here, we investigate the ability of RNA-targeted degradation to improve the selectivity of small molecules targeting RNA. The microRNA-210 hairpin precursor (pre-miR-210) is overexpressed in hypoxic cancers. Previously, a small molecule (Targapremir-210 [TGP-210]) targeted this RNA in cells, but with a 5-fold window for DNA binding. Appendage of a nuclease recruitment module onto TGP-210 locally recruited ribonuclease L onto pre-miR-210, triggering its degradation. The chimera has enhanced selectivity compared with TGP-210 with nanomolar binding to the pre-miR-210, but no DNA binding, and is broadly selective for affecting RNA function in cells. Importantly, it cleaved pre-miR-210 substoichiometrically and induced apoptosis in breast cancer cells.
Myotonic dystrophy type 2 (DM2) is a genetically defined muscular dystrophy that is caused by an expanded repeat of r(CCUG) [r(CCUG)exp] in intron 1 of a CHC-type zinc finger nucleic acid binding protein (CNBP) pre-mRNA. Various mechanisms contribute to DM2 pathology including pre-mRNA splicing defects caused by sequestration of the RNA splicing regulator muscleblind-like-1 (MBNL1) by r(CCUG)exp. Herein, we study the biological impacts of the molecular recognition of r(CCUG)exp's structure by a designer dimeric small molecule that directly cleaves the RNA in patient-derived cells. The compound is comprised of two RNA-binding modules conjugated to a derivative of the natural product bleomycin. Careful design of the chimera affords RNA-specific cleavage, as attachment of the bleomycin cleaving module was done in a manner that disables DNA cleavage. The chimeric cleaver is more potent than the parent binding compound for alleviating DM2-associated defects. Importantly, oligonucleotides targeting the r(CCUG)exp sequence for cleavage exacerbate DM2 defects due to recognition of a short r(CCUG) sequence that is embedded in CNBP, argonaute-1 (AGO1), and MBNL1, reducing their levels. The latter event causes a greater depletion of functional MBNL1 than the amount already sequestered by r(CCUG)exp. Thus, compounds targeting RNA structures can have functional advantages over oligonucleotides that target the sequence in some disease settings, particularly in DM2.