RNA-small Molecule Interactions Research Articles

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

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

Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries

The large-scale analysis of small-molecule binding to diverse RNA structures is key to understanding the required interaction properties and selectivity for developing RNA-binding molecules toward RNA-targeted therapies. Here, we report a new system for performing the large-scale analysis of small molecule–RNA interactions using a multiplexed pull-down assay with RNA structure libraries. The system profiled the RNA-binding landscapes of G-clamp and thiazole orange derivatives, which recognizes an unpaired guanine base and are good probes for fluorescent indicator displacement (FID) assays, respectively. We discuss the binding preferences of these molecules based on their large-scale affinity profiles. In addition, we selected combinations of fluorescent indicators and different ranks of RNA based on the information and screened for RNA-binding molecules using FID. RNAs with high- and intermediate-rank RNA provided reliable results. Our system provides fundamental information about small molecule–RNA interactions and facilitates the discovery of novel RNA-binding molecules.

Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

Probing RNA-Small Molecule Interactions Using Biophysical and Computational Approaches.

Interest in small molecules that target RNA is flourishing, and the expectation set on them to treat diseases with unmet medical needs is high. However, several challenges remain, including difficulties in selecting suitable tools and establishing workflows for their discovery. In this context, we optimized experimental and computational approaches that were previously employed for the protein targets. Here, we demonstrate that a fluorescence-based assay can be effectively used to screen small molecule libraries for their ability to bind and stabilize an RNA stem-loop. Our screen identified several fluoroquinolones that bind to the target stem-loop. We further probed their interactions with the target using biolayer interferometry, isothermal titration calorimetry (ITC), and nuclear magnetic resonance spectroscopy. The results of these biophysical assays suggest that the fluoroquinolones bind the target in a similar manner. Armed with this knowledge, we built models for the complexes of the fluoroquinolones and the RNA target. Then, we performed fragment molecular orbital (FMO) calculations to dissect the interactions between the fluoroquinolones and the RNA. We found that the binding free energies obtained from the ITC experiments correlated strongly with the interaction energies calculated by FMO. Finally, we designed fluoroquinolone analogues and performed FMO calculations to predict their binding free energies. Taken together, the results of this study support the importance of conducting orthogonal assays in binding confirmation and compound selection and demonstrate the usefulness of FMO calculations in the rational design of RNA-targeted small molecules.

Exosome-derived long noncoding RNAs: Mediators of host-Plasmodium parasite communication.

Overcoming challenges associated with malaria eradication proves to be a formidable task due to the complicated life cycle exhibited by the malaria parasite and the lack of safe and enduring vaccines against malaria. Investigating the interplay between Plasmodium parasites and their mammalian hosts is crucial for the development of novel vaccines. Long noncoding RNAs (lncRNAs) derived from Plasmodium parasites or host cells have emerged as potential signaling molecules involved in the trafficking of proteins, RNA (mRNAs, miRNAs, and ncRNAs), and DNA. These lncRNAs facilitate the interaction between hosts and parasites, impacting normal physiology or pathology in malaria-infected individuals. Moreover, they possess the capacity to regulate immune responses and associated signaling pathways, thus potentially influencing chromatin organization, epigenetic modifications, mRNA processing, splicing, and translation. However, the functional role of exosomal lncRNAs in malaria remains poorly understood. This review offers a comprehensive analysis of lncRNA and exosomal lncRNA profiles during malaria infection. It presents an overview of recent progress in elucidating the involvement of exosomal lncRNAs in host-parasite interactions. Additionally, potential exosomal lncRNAs linked to the domains of innate and adaptive immunity in the context of malaria are proposed. These findings may contribute to the discovery of new diagnostic and therapeutic strategies for malaria. Furthermore, the need for additional research was highlighted that aimed to elucidate the mechanisms underlying lncRNA transportation into host cells and their targeting of specific genes to regulate the host's immune response. This knowledge gap presents an opportunity for future investigations, offering innovative approaches to enhance malarial control. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA in Disease and Development > RNA in Disease.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Triplex-Forming Peptide Nucleic Acid Controls Dynamic Conformations of RNA Bulges.

RNA folding is driven by the formation of double-helical segments interspaced by loops of unpaired nucleotides. Among the latter, bulges formed by one or several unpaired nucleotides are one of the most common structural motifs that play an important role in stabilizing RNA-RNA, RNA-protein, and RNA-small molecule interactions. Single-nucleotide bulges can fold in alternative structures where the unpaired nucleobase is either looped-out (flexible) in a solvent or stacked-in (intercalated) between the base pairs. In the present study, we discovered that triplex-forming peptide nucleic acids (PNAs) had unusually high affinity for single-purine-nucleotide bulges in double-helical RNA. Depending on the PNA's sequence, the triplex formation shifted the equilibrium between looped-out and stacked-in conformations. The ability to control the dynamic equilibria of RNA's structure will be an important tool for studying structure-function relationships in RNA biology and may have potential in novel therapeutic approaches targeting disease-related RNAs.

R-SIM: A Database of Binding Affinities for RNA-small Molecule Interactions

Ribonucleic acids (RNAs) are involved in a multitude of crucial cellular functions by acting as a central conduit for information transfer. Due to their essential and versatile functional roles in the cell, RNAs have also been implicated in multiple disease conditions of therapeutic relevance including cancers, bacterial and viral infections and neurodegenerative disorders. Recently, several approaches have emerged to tap into the potentially unexplored regions of the druggable genome, which refers to the genes and gene products that are focused during drug development. For example, considering RNAs as viable alternative therapeutic targets for drug development can potentially expand the range of therapeutic targets. Consequently, the availability of adequate binding affinity measurements for RNA-small molecule interactions is essential to understand target selectivity and design more potent RNA-targeting drug-like molecules. To facilitate this growing need, we have curated a database of experimentally validated RNA-small molecule interactions, called RNA-Small molecule Interaction Miner (R-SIM). Each entry in R-SIM provides comprehensive information on sequence, structure and classification of the RNA target, various physicochemical properties of the small molecule, binding affinity value and corresponding experimental conditions, three-dimensional structure (experimental or modelled) of the RNA-small molecule complex, and the literature source for the data. It also provides a user-friendly web interface with several options for search, display, sorting, visualization, download and upload of the data. R-SIM is freely available at: https://web.iitm.ac.in/bioinfo2/R_SIM/index.html. We envisage that R-SIM has several potential applications in understanding and accelerating the development of novel RNA-targeted small molecule therapeutics.

Targeting DEAD-box RNA helicases: The emergence of molecular staples.

RNA helicases constitute a large family of proteins that play critical roles in mediating RNA function. They have been implicated in all facets of gene expression pathways involving RNA, from transcription to processing, transport and translation, and storage and decay. There is significant interest in developing small molecule inhibitors to RNA helicases as some family members have been documented to be dysregulated in neurological and neurodevelopment disorders, as well as in cancers. Although different functional properties of RNA helicases offer multiple opportunities for small molecule development, molecular staples have recently come to the forefront. These bifunctional molecules interact with both protein and RNA components to lock them together, thereby imparting novel gain-of-function properties to their targets. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Interrogating RNA-Small Molecule Interactions with Structure Probing and Artificial Intelligence-Augmented Molecular Simulations.

While there is increasing interest in the study of RNA as a therapeutic target, efforts to understand RNA-ligand recognition at the molecular level lag far behind our understanding of protein–ligand recognition. This problem is complicated due to the more than 10 orders of magnitude in time scales involved in RNA dynamics and ligand binding events, making it not straightforward to design experiments or simulations. Here, we make use of artificial intelligence (AI)-augmented molecular dynamics simulations to directly observe ligand dissociation for cognate and synthetic ligands from a riboswitch system. The site-specific flexibility profiles from our simulations are compared with in vitro measurements of flexibility using selective 2′ hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP). Our simulations reproduce known relative binding affinity profiles for the cognate and synthetic ligands, and pinpoint how both ligands make use of different aspects of riboswitch flexibility. On the basis of our dissociation trajectories, we also make and validate predictions of pairs of mutations for both the ligand systems that would show differing binding affinities. These mutations are distal to the binding site and could not have been predicted solely on the basis of structure. The methodology demonstrated here shows how molecular dynamics simulations with all-atom force-fields have now come of age in making predictions that complement existing experimental techniques and illuminate aspects of systems otherwise not trivial to understand.

Deep structural insights into RNA-binding disordered protein regions.

Recent efforts to identify RNA binding proteins in various organisms and cellular contexts have yielded a large collection of proteins that are capable of RNA binding in the absence of conventional RNA recognition domains. Many of the recently identified RNA interaction motifs fall into intrinsically disordered protein regions (IDRs). While the recognition mode and specificity of globular RNA binding elements have been thoroughly investigated and described, much less is known about the way IDRs can recognize their RNA partners. Our aim was to summarize the current state of structural knowledge on the RNA binding modes of disordered protein regions and to propose a classification system based on their sequential and structural properties. Through a detailed structural analysis of the complexes that contain disordered protein regions binding to RNA, we found two major binding modes that represent different recognition strategies and, most likely, functions. We compared these examples with DNA binding disordered proteins and found key differences stemming from the nucleic acids as well as similar binding strategies, implying a broader substrate acceptance by these proteins. Due to the very limited number of known structures, we integrated molecular dynamics simulations in our study, whose results support the proposed structural preferences of specific RNA‐binding IDRs. To broaden the scope of our review, we included a brief analysis of RNA‐binding small molecules and compared their structural characteristics and RNA recognition strategies to the RNA‐binding IDRs.This article is categorized under:RNA Structure and Dynamics > RNA Structure, Dynamics, and ChemistryRNA Interactions with Proteins and Other Molecules > Protein–RNA RecognitionRNA Interactions with Proteins and Other Molecules > Small Molecule–RNA Interactions

Riboregulation in bacteria: From general principles to novel mechanisms of the trp attenuator and its sRNA and peptide products.

Gene expression strategies ensuring bacterial survival and competitiveness rely on cis- and trans-acting RNA-regulators (riboregulators). Among the cis-acting riboregulators are transcriptional and translational attenuators, and antisense RNAs (asRNAs). The trans-acting riboregulators are small RNAs (sRNAs) that bind proteins or base pairs with other RNAs. This classification is artificial since some regulatory RNAs act both in cis and in trans, or function in addition as small mRNAs. A prominent example is the archetypical, ribosome-dependent attenuator of tryptophan (Trp) biosynthesis genes. It responds by transcription attenuation to two signals, Trp availability and inhibition of translation, and gives rise to two trans-acting products, the attenuator sRNA rnTrpL and the leader peptide peTrpL. In Escherichia coli, rnTrpL links Trp availability to initiation of chromosome replication and in Sinorhizobium meliloti, it coordinates regulation of split tryptophan biosynthesis operons. Furthermore, in S. meliloti, peTrpL is involved in mRNA destabilization in response to antibiotic exposure. It forms two types of asRNA-containing, antibiotic-dependent ribonucleoprotein complexes (ARNPs), one of them changing the target specificity of rnTrpL. The posttranscriptional role of peTrpL indicates two emerging paradigms: (1) sRNA reprograming by small molecules and (2) direct involvement of antibiotics in regulatory RNPs. They broaden our view on RNA-based mechanisms and may inspire new approaches for studying, detecting, and using antibacterial compounds. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

A chemical probe based on the PreQ1 metabolite enables transcriptome-wide mapping of binding sites

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.

RNA-ligand molecular docking: advances and challenges.

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

Time to infer miRNA sponge modules.

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

PEARL-seq: A Workflow for Analyzing Small Molecule–RNA Interactions

PEARL-seq: A Workflow for Analyzing Small Molecule–RNA Interactions

PEARL-seq: A Photoaffinity Platform for the Analysis of Small Molecule-RNA Interactions.

RNA is emerging as a valuable target for the development of novel therapeutic agents. The rational design of RNA-targeting small molecules, however, has been hampered by the relative lack of methods for the analysis of small molecule-RNA interactions. Here, we present our efforts to develop such a platform using photoaffinity labeling. This technique, termed Photoaffinity Evaluation of RNA Ligation-Sequencing (PEARL-seq), enables the rapid identification of small molecule binding locations within their RNA targets and can provide information on ligand selectivity across multiple different RNAs. These data, when supplemented with small molecule SAR data and RNA probing data enable the construction of a computational model of the RNA-ligand structure, thereby enabling the rational design of novel RNA-targeted ligands.

Methods for spatial and temporal imaging of the different steps involved in RNA processing at single-molecule resolution.

RNA plays a quintessential role as a messenger of information from genotype (DNA) to phenotype (proteins), as well as acts as a regulatory molecule (noncoding RNAs). All steps in the journey of RNA from synthesis (transcription), splicing, transport, localization, translation, to its eventual degradation, comprise important steps in gene expression, thereby controlling the fate of the cell. This lifecycle refers to the majority of RNAs (primarily mRNAs), but not other RNAs such as tRNAs. Imaging these processes in fixed cells and in live cells has been an important tool in developing an understanding of the regulatory steps in RNAs journey. Single-cell and single-molecule imaging techniques enable a much deeper understanding of cellular biology, which is not possible with bulk studies involving RNA isolated from a large pool of cells. Classic techniques, such as fluorescence in situ hybridization (FISH), as well as more recent aptamer-based approaches, have provided detailed insights into RNA localization, and have helped to predict the functions carried out by many RNA species. However, there are still certain processing steps that await high-resolution imaging, which is an exciting and upcoming area of research. In this review, we will discuss the methods that have revolutionized single-molecule resolution imaging in general, the steps of RNA processing in which these methods have been used, and new emerging technologies. This article is categorized under: RNA Export and Localization > RNA Localization RNA Methods > RNA Analyses in Cells RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions.

The powerful world of antisense oligonucleotides: From bench to bedside.

Antisense oligonucleotides (ASOs) represent a new and highly promising class of drugs for personalized medicine. In the last decade, major chemical developments and improvements of the backbone structure of ASOs have transformed them into true approved and commercialized drugs. ASOs target both DNA and RNA, including pre‐mRNA, mRNA, and ncRDA, based on sequence complementary. They are designed to be specific for each identified molecular and genetic alteration to restore a normal, physiological situation. Thus, the characterization of the underpinning mechanisms and alterations that sustain pathology is critical for accurate ASO‐design. ASOs can be used to cure both rare and common diseases, such as orphan genetic alterations and cancer. Through pioneering examples, this review shows the versatility of the mechanisms of action that provide ASOs with the potential capacity to achieve custom treatment, revolutionizing personalized medicine.This article is categorized under:RNA in Disease and Development > RNA in DiseaseRNA Interactions with Proteins and Other Molecules > Small Molecule–RNA Interactions