How We Think about Targeting RNA with Small Molecules

Antisense RNA C9orf72 hexanucleotide repeat associated with amyotrophic lateral sclerosis and frontotemporal dementia forms a triplex-like structure and binds small synthetic ligand

The abnormal expansion of GGGGCC/GGCCCC hexanucleotide repeats (HR) in C9orf72 is associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Structural polymorphisms of HR result in the multifactorial pathomechanism of ALS/FTD. Consequently, many ongoing studies are focused at developing therapies targeting pathogenic HR RNA. One of them involves small molecules blocking sequestration of important proteins, preventing formation of toxic nuclear foci. However, rational design of potential therapeutics is hindered by limited number of structural studies of RNA-ligand complexes. We determined the crystal structure of antisense HR RNA in complex with ANP77 ligand (1.1 Å resolution) and in the free form (0.92 and 1.5 Å resolution). HR RNA folds into a triplex structure composed of four RNA chains. ANP77 interacted with two neighboring single-stranded cytosines to form pseudo-canonical base pairs by adopting sandwich-like conformation and adjusting the position of its naphthyridine units to the helical twist of the RNA. In the unliganded structure, the cytosines formed a peculiar triplex i-motif, assembled by trans C•C+ pair and a third cytosine located at the Hoogsteen edge of the C•C+ pair. These results extend our knowledge of the structural polymorphisms of HR and can be used for rational design of small molecules targeting disease-related RNAs.

Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding. However, RNAs are still considered challenging targets due to their complex structural dynamics and high charge density. Thus, elucidating relevant features of drug-RNA binding is fundamental for advancing drug discovery. Here, by using Molecular Dynamics simulations, we compare key features of ligand binding to proteins with those observed in RNA. Specifically, we explore similarities and differences in terms of (i) conformational flexibility of the target, (ii) electrostatic contribution to binding free energy, and (iii) water and ligand dynamics. As a test case, we examine binding of the same ligand, namely riboflavin, to protein and RNA targets, specifically the riboflavin (RF) kinase and flavin mononucleotide (FMN) riboswitch. The FMN riboswitch exhibited enhanced fluctuations and explored a wider conformational space, compared to the protein target, underscoring the importance of RNA flexibility in ligand binding. Conversely, a similar electrostatic contribution to the binding free energy of riboflavin was found. Finally, greater stability of water molecules was observed in the FMN riboswitch compared to the RF kinase, possibly due to the different shape and polarity of the pockets.

Advances in machine-learning approaches to RNA-targeted drug design

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

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.

The prognostic, diagnostic, and therapeutic impact of Long noncoding RNAs in gastric cancer

Gastric cancer (GC), ranking as the third deadliest cancer globally, faces challenges of late diagnosis and limited treatment efficacy. Long non-coding RNAs (lncRNAs) emerge as valuable treasured targets for cancer prognosis, diagnosis, and therapy, given their high specificity, convenient non-invasive detection in body fluids, and crucial roles in diverse physiological and pathological processes. Research indicates the significant involvement of lncRNAs in various aspects of GC pathogenesis, including initiation, metastasis, and recurrence, underscoring their potential as novel diagnostic and prognostic biomarkers, as well as therapeutic targets for GC. Despite existing challenges in the clinical application of lncRNAs in GC, the evolving landscape of lncRNA molecular biology holds promise for advancing the survival and treatment outcomes of gastric cancer patients. This review provides insights into recent studies on lncRNAs in gastric cancer, elucidating their molecular mechanisms and exploring the potential clinical applications in GC.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction

MOTIVATION

The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately.

RESULTS

In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SaisaiSun/RLaffinity.

RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag

RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.

Targeting MicroRNAs with Small Molecules

MicroRNAs (miRs) have been implicated in numerous diseases, presenting an attractive target for the development of novel therapeutics. The various regulatory roles of miRs in cellular processes underscore the need for precise strategies. Recent advances in RNA research offer hope by enabling the identification of small molecules capable of selectively targeting specific disease-associated miRs. This understanding paves the way for developing small molecules that can modulate the activity of disease-associated miRs. Herein, we discuss the progress made in the field of drug discovery processes, transforming the landscape of miR-targeted therapeutics by small molecules. By leveraging various approaches, researchers can systematically identify compounds to modulate miR function, providing a more potent intervention either by inhibiting or degrading miRs. The implementation of these multidisciplinary approaches bears the potential to revolutionize treatments for diverse diseases, signifying a significant stride towards the targeting of miRs by precision medicine.

Emerging Futuristic Targeted Therapeutics: A Comprising Study Towards a New Era for the Management of TNBC

Triple-negative breast cancer is characterized by high lethality attributed to factors such as chemoresistance, transcriptomic, and genomic heterogeneity, leading to a poor prognosis and limiting available targeted treatment options. While the identification of molecular targets remains pivotal for therapy involving chemo drugs, the current challenge lies in the poor response rates, low survival rates, and frequent relapses. Despite various clinical investigations exploring molecular targeted therapies in conjunction with conventional chemo treatment, the outcomes have been less than optimal. The critical need for more effective therapies underscores the urgency to discover potent novel treatments, including molecular and immune targets, as well as emerging strategies. This review provides a comprehensive analysis of conventional treatment approaches and explores emerging molecular and immune-targeted therapeutics, elucidating their mechanisms to address the existing obstacles for a more effective management of triple-negative breast cancer.

Targeting and engineering long non-coding RNAs for cancer therapy

RNA therapeutics (RNATx) aim to treat diseases, including cancer, by targeting or employing RNA molecules for therapeutic purposes. Amongst the most promising targets are long non-coding RNAs (lncRNAs), which regulate oncogenic molecular networks in a cell type-restricted manner. lncRNAs are distinct from protein-coding genes in important ways that increase their therapeutic potential yet also present hurdles to conventional clinical development. Advances in genome editing, oligonucleotide chemistry, multi-omics and RNA engineering are paving the way for efficient and cost-effective lncRNA-focused drug discovery pipelines. In this Review, we present the emerging field of lncRNA therapeutics for oncology, with emphasis on the unique strengths and challenges of lncRNAs within the broader RNATx framework. We outline the necessary steps for lncRNA therapeutics to deliver effective, durable, tolerable and personalized treatments for cancer.

Specificity, synergy, and mechanisms of splice-modifying drugs

Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs work is limited. Here we introduce mechanistically interpretable quantitative models for the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we obtain quantitative models for two small-molecule drugs, risdiplam and branaplam, developed for treating spinal muscular atrophy. The results quantitatively characterize the specificities of risdiplam and branaplam for 5' splice site sequences, suggest that branaplam recognizes 5' splice sites via two distinct interaction modes, and contradict the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous single-drug cooperativity, as well as multi-drug synergy, are widespread among small-molecule drugs and antisense-oligonucleotide drugs that promote exon inclusion. Our quantitative models thus clarify the mechanisms of existing treatments and provide a basis for the rational development of new therapies.

Recent Advances in RNA-Targeted Cancer Therapy

Ribonucleic acid (RNA) plays a pivotal role in gene regulation and protein biosynthesis. Interfering the physiological function of key RNAs to induce cell apoptosis holds great promise for cancer treatment. Many RNA-targeted anti-cancer strategies have emerged continuously. Among them, RNA interference (RNAi) has been recognized as a promising therapeutic modality for various disease treatments. Nevertheless, the primary obstacle in siRNA delivery-escaping the endosome and crossing the plasma membrane severely impedes its therapeutic potential. Thus far, a variety of nanosystems as well as carrier-free bioconjugation for siRNA delivery have been developed and employed to enhance the drug delivery and anti-tumor efficiency. Besides, the use of small molecules to target specific RNA structures and disrupt their function, along with the covalent modification of RNA, has also drawn tremendous attention recently owing to high therapeutic efficacy. In this review, we will provide an overview of recent progress in RNA-targeted cancer therapy including various siRNA delivery strategies, RNA-targeting small molecules, and newly emerged covalent RNA modification. Finally, challenges and future perspectives faced in this research field will be discussed.

A computational method for small molecule-RNA binding sites identification by utilizing position specificity and complex network information

Some computational methods have been given for small molecule-RNA binding site identification due to that it plays a significant role in revealing biology function researches. However, it is still challenging to design an accurate model, especially for MCC. We designed a feature extraction technology from two aspects (position specificity and complex network information). Specifically, complex network was employed to express the space topological structure and sequence position information for improving prediction effect. Then, the features fused position specificity and complex network information were input into random forest classifier for model construction. The AUC of 88.22%, 77.92% and 81.46% were obtained on three independent datasets (RB19, CS71, RB78). Compared with the existing method, the best MCC were obtained on three datasets, which were 8.19%, 0.59% and 4.35% higher than the state-of-the-art prediction methods, respectively. The outstanding performances show that our method is a powerful tool to identify RNA binding sites, helping to the design RNA-targeting small molecule drugs. The data and resource codes are available at https://github.com/Kangxiaoneuq/PCN_RNAsite.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking

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

Using the structural diversity of RNA: protein interfaces to selectively target RNA with small molecules in cells: methods and perspectives

In recent years, RNA has gained traction both as a therapeutic molecule and as a therapeutic target in several human pathologies. In this review, we consider the approach of targeting RNA using small molecules for both research and therapeutic purposes. Given the primary challenge presented by the low structural diversity of RNA, we discuss the potential for targeting RNA: protein interactions to enhance the structural and sequence specificity of drug candidates. We review available tools and inherent challenges in this approach, ranging from adapted bioinformatics tools to in vitro and cellular high-throughput screening and functional analysis. We further consider two critical steps in targeting RNA/protein interactions: first, the integration of in silico and structural analyses to improve the efficacy of molecules by identifying scaffolds with high affinity, and second, increasing the likelihood of identifying on-target compounds in cells through a combination of high-throughput approaches and functional assays. We anticipate that the development of a new class of molecules targeting RNA: protein interactions to prevent physio-pathological mechanisms could significantly expand the arsenal of effective therapeutic compounds.

Estrogen-Induced LncRNA, LINC02568, Promotes Estrogen Receptor-Positive Breast Cancer Development and Drug Resistance Through Both In Trans and In Cis Mechanisms

Endocrine therapy is the frontline treatment for estrogen receptor (ER) positive breast cancer patients. However, the primary and acquired resistance to endocrine therapy drugs remain as a major challenge in the clinic. Here, this work identifies an estrogen-induced lncRNA, LINC02568, which is highly expressed in ER-positive breast cancer and functional important in cell growth in vitro and tumorigenesis in vivo as well as endocrine therapy drug resistance. Mechanically, this work demonstrates that LINC02568 regulates estrogen/ERα-induced gene transcriptional activation in trans by stabilizing ESR1 mRNA through sponging miR-1233-5p in the cytoplasm. Meanwhile, LINC02568 contributes to tumor-specific pH homeostasis by regulating carbonic anhydrase CA12 in cis in the nucleus. The dual functions of LINC02568 together contribute to breast cancer cell growth and tumorigenesis as well as endocrine therapy drug resistance. Antisense oligonucleotides (ASO) targeting LINC02568 significantly inhibits ER-positive breast cancer cell growth in vitro and tumorigenesis in vivo. Furthermore, combination treatment with ASO targeting LINC02568 and endocrine therapy drugs or CA12 inhibitor U-104 exhibits synergistic effects on tumor growth. Taken together, the findings reveal the dual mechanisms of LINC02568 in regulating ERα signaling and pH homeostasis in ER-positive breast cancer, and indicated that targeting LINC02568 might represent a potential therapeutic avenue in the clinic.

Small Molecules Targeting Viral RNA

The majority of antivirals available target viral proteins; however, RNA is emerging as a new and promising antiviral target due to the presence of highly structured RNA in viral genomes fundamental for their replication cycle. Here, we discuss methods for the identification of RNA-targeting compounds, starting from the determination of RNA structures either from purified RNA or in living cells, followed by in silico screening on RNA and phenotypic assays to evaluate viral inhibition. Moreover, we review the small molecules known to target the programmed ribosomal frameshifting element of SARS-CoV-2, the internal ribosomal entry site of different viruses, and RNA elements of HIV.

DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases

There has always been a tendency of medicine to take an individualised approach to treating patients, but the most significant advances were achieved through the methods of molecular biology, where the nucleic acids are in the limelight. Decades of research of molecular biology resulted in setting medicine on a completely new platform. The most significant current research is related to the possibilities that DNA and RNA analyses can offer in terms of more precise diagnostics and more subtle stratification of patients in order to identify patients for specific therapy treatments. Additionally, principles of structure and functioning of nucleic acids have become a motive for creating entirely new therapy strategies and an innovative generation of drugs. All this also applies to cardiovascular diseases (CVDs) which are the leading cause of mortality in developed countries. This review considers the most up-to-date achievements related to the use of translatory potential of DNA and RNA in treatment of cardiovascular diseases, and considers the challenges and prospects in this field. The foundations which allow the use of translatory potential are also presented. The first part of this review focuses on the potential of the DNA variants which impact conventional therapies and on the DNA variants which are starting points for designing new pharmacotherapeutics. The second part of this review considers the translatory potential of non-coding RNA molecules which can be used to formulate new generations of therapeutics for CVDs.

Amplifying gene expression with RNA-targeted therapeutics

Many diseases are caused by insufficient expression of mutated genes and would benefit from increased expression of the corresponding protein. However, in drug development, it has been historically easier to develop drugs with inhibitory or antagonistic effects. Protein replacement and gene therapy can achieve the goal of increased protein expression but have limitations. Recent discoveries of the extensive regulatory networks formed by non-coding RNAs offer alternative targets and strategies to amplify the production of a specific protein. In addition to RNA-targeting small molecules, new nucleic acid-based therapeutic modalities that allow highly specific modulation of RNA-based regulatory networks are being developed. Such approaches can directly target the stability of mRNAs or modulate non-coding RNA-mediated regulation of transcription and translation. This Review highlights emerging RNA-targeted therapeutics for gene activation, focusing on opportunities and challenges for translation to the clinic.

Programming inactive RNA-binding small molecules into bioactive degraders

Target occupancy is often insufficient to elicit biological activity, particularly for RNA, compounded by the longstanding challenges surrounding the molecular recognition of RNA structures by small molecules. Here we studied molecular recognition patterns between a natural-product-inspired small-molecule collection and three-dimensionally folded RNA structures. Mapping these interaction landscapes across the human transcriptome defined structure-activity relationships. Although RNA-binding compounds that bind to functional sites were expected to elicit a biological response, most identified interactions were predicted to be biologically inert as they bind elsewhere. We reasoned that, for such cases, an alternative strategy to modulate RNA biology is to cleave the target through a ribonuclease-targeting chimera, where an RNA-binding molecule is appended to a heterocycle that binds to and locally activates RNase L1. Overlay of the substrate specificity for RNase L with the binding landscape of small molecules revealed many favourable candidate binders that might be bioactive when converted into degraders. We provide a proof of concept, designing selective degraders for the precursor to the disease-associated microRNA-155 (pre-miR-155), JUN mRNA and MYC mRNA. Thus, small-molecule RNA-targeted degradation can be leveraged to convert strong, yet inactive, binding interactions into potent and specific modulators of RNA function.

Targeting genomic DNAs and oligonucleotide on base specificity: A comparative spectroscopic, computational and in vitro study

Drug discovery in targeted nucleic acid therapeutics encompass several stages and rigorous challenges owing to less specificity of the DNA binders and high failure rate in different stages of clinical trials. In this perspective, we report newly synthesized ethyl 4-(pyrrolo[1,2-a]quinolin-4-yl)benzoate (PQN) with minor groove A-T base pair binding selectivity and encouraging in cell results. This pyrrolo quinolin derivative has shown excellent groove binding ability with three of our inspected genomic DNAs (cpDNA 73 % AT, ctDNA58% AT and mlDNA 28 % AT) with varying A-T and G-C content. Notably in spite of similar binding patterns PQN have strong binding preference with A-T rich groove of genomic cpDNA over the ctDNA and mlDNA. Spectroscopic experiments like steady state absorption and emission results have established the relative binding strengths (K_abs = 6.3 × 10⁵ M^-1, 5.6 × 10⁴ M^-1, 4.3 × 10⁴ M^-1 and K_emiss = 6.1 × 10⁵ M^-1, 5.7 × 10⁴ M^-1 and 3.5 × 10⁴ M^-1 for PQN-cpDNA, PQN-ctDNA and PQN-mlDNA respectively) whereas circular dichroism and thermal melting studies have unveiled the groove binding mechanism. Specific A-T base pair attachment with van der Waals interaction and quantitative hydrogen bonding assessment were characterized by computational modeling. In addition to genomic DNAs, preferential A-T base pair binding in minor groove was also observed with our designed and synthesized deca-nucleotide (primer sequences 5^/-GCGAATTCGC-3^/ and 3^/-CGCTTAAGCG-5^/). Cell viability assays (86.13 % in 6.58 μM and 84.01 % in 9.88 μM concentrations) and confocal microscopy revealed low cytotoxicity (IC₅₀ 25.86 μM) and efficient perinuclear localization of PQN. We propose PQN with excellent DNA-minor groove binding capacity and intracellular permeation properties, as a lead for further studies encompassing nucleic acid therapeutics.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Long non-coding RNAs and gastric cancer: An update of potential biomarkers and therapeutic applications

The frequent metastasis of gastric cancer (GC) complicates the cure and therefore the development of effective diagnostic and therapeutic approaches is urgently necessary. In recent years, lncRNA has emerged as a drug target in the treatment of GC, particularly in the areas of cancer immunity, cancer metabolism, and cancer metastasis. This has led to the demonstration of the importance of these RNAs as prognostic, diagnostic and therapeutic agents. In this review, we provide an overview of the biological activities of lncRNAs in GC development and update the latest pathological activities, prognostic and diagnostic strategies, and therapeutic options for GC-related lncRNAs.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Contemporary Progress and Opportunities in RNA-Targeted Drug Discovery

The surprising discovery that RNAs are the predominant gene products to emerge from the human genome catalyzed a renaissance in RNA biology. It is now well-understood that RNAs act as more than just a messenger and comprise a large and diverse family of ribonucleic acids of differing sizes, structures, and functions. RNAs play expansive roles in the cell, contributing to the regulation and fine-tuning of nearly all aspects of gene expression and genome architecture. In line with the significance of these functions, we have witnessed an explosion in discoveries connecting RNAs with a variety of human diseases. Consequently, the targeting of RNAs, and more broadly RNA biology, has emerged as an untapped area of drug discovery, making the search for RNA-targeted therapeutics of great interest. In this Microperspective, I highlight contemporary learnings in the field and present my views on how to catapult us toward the systematic discovery of RNA-targeted medicines.

Protein-RNA interactions: from mass spectrometry to drug discovery

Proteins and RNAs are fundamental parts of biological systems, and their interactions affect many essential cellular processes. Therefore, it is crucial to understand at a molecular and at a systems level how proteins and RNAs form complexes and mutually affect their functions. In the present mini-review, we will first provide an overview of different mass spectrometry (MS)-based methods to study the RNA-binding proteome (RBPome), most of which are based on photochemical cross-linking. As we will show, some of these methods are also able to provide higher-resolution information about binding sites, which are important for the structural characterisation of protein-RNA interactions. In addition, classical structural biology techniques such as nuclear magnetic resonance (NMR) spectroscopy and biophysical methods such as electron paramagnetic resonance (EPR) spectroscopy and fluorescence-based methods contribute to a detailed understanding of the interactions between these two classes of biomolecules. We will discuss the relevance of such interactions in the context of the formation of membrane-less organelles (MLOs) by liquid-liquid phase separation (LLPS) processes and their emerging importance as targets for drug discovery.

Emerging regulatory mechanisms of noncoding RNAs in topologically associating domains

Topologically associating domains (TADs) are integral to spatial genome organization, instructing gene expression, and cell fate. Recently, several advances have uncovered roles for noncoding RNAs (ncRNAs) in the regulation of the form and function of mammalian TADs. Phase separation has also emerged as a potential arbiter of ncRNAs in the regulation of TADs. In this review we discuss the implications of these novel findings in relation to how ncRNAs might structurally and functionally regulate TADs from two perspectives: moderating loop extrusion through interactions with architectural proteins, and facilitating TAD phase separation. Additionally, we propose future studies and directions to investigate these phenomena.

RNA splicing dysregulation and the hallmarks of cancer

Dysregulated RNA splicing is a molecular feature that characterizes almost all tumour types. Cancer-associated splicing alterations arise from both recurrent mutations and altered expression of trans-acting factors governing splicing catalysis and regulation. Cancer-associated splicing dysregulation can promote tumorigenesis via diverse mechanisms, contributing to increased cell proliferation, decreased apoptosis, enhanced migration and metastatic potential, resistance to chemotherapy and evasion of immune surveillance. Recent studies have identified specific cancer-associated isoforms that play critical roles in cancer cell transformation and growth and demonstrated the therapeutic benefits of correcting or otherwise antagonizing such cancer-associated mRNA isoforms. Clinical-grade small molecules that modulate or inhibit RNA splicing have similarly been developed as promising anticancer therapeutics. Here, we review splicing alterations characteristic of cancer cell transcriptomes, dysregulated splicing's contributions to tumour initiation and progression, and existing and emerging approaches for targeting splicing for cancer therapy. Finally, we discuss the outstanding questions and challenges that must be addressed to translate these findings into the clinic.

Translating the Genome into Drugs

The Human Genome Project ultimately aimed to translate DNA sequence into drugs. With the draft in hand, the Molecular Libraries Program set out to prosecute all genome-encoded proteins for drug discovery with automated high-throughput screening (HTS). This ambitious vision remains unfulfilled, even while innovations in sequencing technology have fully democratized access to genome-scale sequencing. Why? While the central dogma of biology allows us to chart the entirety of cellular metabolism through sequencing, there is no direct coding for chemistry. The rules of base pairing that relate DNA gene to RNA transcript and amino acid sequence do not exist for relating small-molecule structure with macromolecular binding partners and subsequently cellular function. Obtaining such relationships genome-wide is unapproachable via state-of-the-art HTS, akin to attempting genome-wide association studies using turn-of-the-millennium Sanger DNA sequencing.Our laboratory has been engaged in a multipronged technology development campaign to revolutionize molecular screening through miniaturization in pursuit of genome-scale drug discovery capabilities. The compound library was ripe for miniaturization: it clearly needed to become a consumable. We employed DNA-encoded library (DEL) synthesis principles in the development of solid-phase DELs prepared on microscopic beads, each harboring 100 fmol of a single library member and a DNA tag whose sequence describes the structure of the library member. Loading these DEL beads into 100 pL microfluidic droplets followed by online photocleavage, incubation, fluorescence-activated droplet sorting, and DNA sequencing of the sorted DEL beads reveals the chemical structures of bioactive compounds. This scalable library synthesis and screening platform has proven useful in several proof-of-concept projects involving current clinical targets.Moving forward, we face the problem of druggability and proteome-scale assay development. Developing biochemical or cellular assays for all genome-encoded targets is not scalable and likely impossible as most proteins have ill-defined or unknown activity and may not function outside of their native contexts. These are the dark undruggable expanses, and charting them will require advanced synthesis and analytical technologies that can generalize probe discovery, irrespective of mature protein function, to fulfill the Genome Project's vision of proteome-wide control of cellular pharmacology.

Brief considerations on targeting RNA with small molecules

For more than three decades, RNA has been known to be a relevant and attractive macromolecule to target but figuring out which RNA should be targeted and how remains challenging. Recent years have seen the confluence of approaches for screening, drug optimization, and target validation that have led to the approval of a few RNA-targeting therapeutics for clinical applications. This focused perspective aims to highlight - but not exhaustively review - key factors accounting for these successes while pointing at crucial aspects worth considering for further breakthroughs.

Inverse In Vitro Selection Enables Comprehensive Analysis of Cross-Chiral L-Aptamer Interactions

Aptamers composed of mirror-image L-(deoxy)ribose nucleic acids, referred to as L-aptamers, are a promising class of RNA-binding reagents. Yet, the selectivity of cross-chiral interactions between L-aptamers and their RNA targets remain poorly characterized, limiting the potential utility of this approach for applications in biological systems. Herein, we carried out the first comprehensive analysis of cross-chiral L-aptamer selectivity using a newly developed "inverse" in vitro selection approach that exploits the genetic nature of the D-RNA ligand. By employing a library of more than a million target-derived sequences, we determined the RNA sequence and structural preference of a model L-aptamer and revealed previously unidentified and potentially broad off-target RNA binding behaviors. These results provide valuable information for assessing the likelihood and consequences of potential off-target interactions and reveal strategies to mitigate these effects. Thus, inverse in vitro selection provides several opportunities to advance L-aptamer technology.

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

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

Progression of Antiviral Agents Targeting Viral Polymerases

Viral DNA and RNA polymerases are two kinds of very important enzymes that synthesize the genetic materials of the virus itself, and they have become extremely favorable targets for the development of antiviral drugs because of their relatively conserved characteristics. There are many similarities in the structure and function of different viral polymerases, so inhibitors designed for a certain viral polymerase have acted as effective universal inhibitors on other types of viruses. The present review describes the development of classical antiviral drugs targeting polymerases, summarizes a variety of viral polymerase inhibitors from the perspective of chemically synthesized drugs and natural product drugs, describes novel approaches, and proposes promising development strategies for antiviral drugs.

G-quadruplex-guided RNA engineering to modulate CRISPR-based genomic regulation

It is important to develop small moelcule-based methods to modulate gene editing and expression in human cells. The roles of the G-quadruplex (G4) in biological systems have been widely studied. Here, G4-guided RNA engineering is performed to generate guide RNA with G4-forming units (G4-gRNA). We further demonstrate that chemical targeting of G4-gRNAs holds promise as a general approach for modulating gene editing and expression in human cells. The rich structural diversity of RNAs offers a reservoir of targets for small molecules to bind, thus creating the potential to modulate RNA biology.

Non-coding RNAs in hepatocellular carcinoma: Insights into regulatory mechanisms, clinical significance, and therapeutic potential

Hepatocellular carcinoma (HCC) is a complex and heterogeneous malignancy with high incidence and poor prognosis. In addition, owing to the lack of diagnostic and prognostic markers, current multimodal treatment options fail to achieve satisfactory outcomes. Tumor immune microenvironment (TIME), angiogenesis, epithelial-mesenchymal transition (EMT), invasion, metastasis, metabolism, and drug resistance are important factors influencing tumor development and therapy. The intercellular communication of these important processes is mediated by a variety of bioactive molecules to regulate pathophysiological processes in recipient cells. Among these bioactive molecules, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), account for a large part of the human transcriptome, and their dysregulation affects the progression of HCC. The purpose of this review is to evaluate the potential regulatory mechanisms of ncRNAs in HCC, summarize novel biomarkers from somatic fluids (plasma/serum/urine), and explore the potential of some small-molecule modulators as drugs. Thus, through this review, we aim to contribute to a deeper understanding of the regulatory mechanisms, early diagnosis, prognosis, and precise treatment of HCC.

Noncoding RNAs Emerging as Drugs or Drug Targets: Their Chemical Modification, Bio-Conjugation and Intracellular Regulation

With the increasing understanding of various disease-related noncoding RNAs, ncRNAs are emerging as novel drugs and drug targets. Nucleic acid drugs based on different types of noncoding RNAs have been designed and tested. Chemical modification has been applied to noncoding RNAs such as siRNA or miRNA to increase the resistance to degradation with minimum influence on their biological function. Chemical biological methods have also been developed to regulate relevant noncoding RNAs in the occurrence of various diseases. New strategies such as designing ribonuclease targeting chimeras to degrade endogenous noncoding RNAs are emerging as promising approaches to regulate gene expressions, serving as next-generation drugs. This review summarized the current state of noncoding RNA-based theranostics, major chemical modifications of noncoding RNAs to develop nucleic acid drugs, conjugation of RNA with different functional biomolecules as well as design and screening of potential molecules to regulate the expression or activity of endogenous noncoding RNAs for drug development. Finally, strategies of improving the delivery of noncoding RNAs are discussed.

Targeting RNA structures with small molecules

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.

Development of Therapeutic Approaches for Myotonic Dystrophies Type 1 and Type 2

Myotonic Dystrophies type 1 (DM1) and type 2 (DM2) are complex multisystem diseases without disease-based therapies. These disorders are caused by the expansions of unstable CTG (DM1) and CCTG (DM2) repeats outside of the coding regions of the disease genes: DMPK in DM1 and CNBP in DM2. Multiple clinical and molecular studies provided a consensus for DM1 pathogenesis, showing that the molecular pathophysiology of DM1 is associated with the toxicity of RNA CUG repeats, which cause multiple disturbances in RNA metabolism in patients' cells. As a result, splicing, translation, RNA stability and transcription of multiple genes are misregulated in DM1 cells. While mutant CCUG repeats are the main cause of DM2, additional factors might play a role in DM2 pathogenesis. This review describes current progress in the translation of mechanistic knowledge in DM1 and DM2 to clinical trials, with a focus on the development of disease-specific therapies for patients with adult forms of DM1 and congenital DM1 (CDM1).

Progress and Challenges in Targeted Protein Degradation for Neurodegenerative Disease Therapy

Neurodegenerative diseases (NDs) are currently incurable diseases that cause progressive degeneration of nerve cells. Many of the disease-causing proteins of NDs are "undruggable" for traditional small-molecule inhibitors (SMIs). None of the compounds that attenuated the amyloid-β (Aβ) accumulation process have entered clinical practice, and many phase III clinical trials of SMIs for Alzheimer's disease (AD) have failed. In recent years, emerging targeted protein degradation (TPD) technologies such as proteolysis-targeting chimeras (PROTACs), lysosome-targeting chimaeras (LYTACs), and autophagy-targeting chimeras (AUTACs) with TPD-assistive technologies such as click-formed proteolysis-targeting chimeras (CLIPTACs) and deubiquitinase-targeting chimera (DUBTAC) have developed rapidly. In vitro and in vivo experiments have also confirmed that TPD technology can target the degradation of ND pathogenic proteins, bringing hope for the treatment of NDs. Herein, we review the latest TPD technologies, introduce their targets and technical characteristics, and discuss the emerging TPD technologies with potential in ND research, with the hope of providing a new perspective for the development of TPD technology in the NDs field.

A Fluorescent Reverse-Transcription Assay to Detect Chemical Adducts on RNA

Herein, we detail a novel reverse-transcription (RT) assay to directly detect chemical adducts on RNA. We optimize a fluorescence quenching assay to detect RT polymerization and employ our approach to detect N¹-alkylation of inosine, an important post-transcriptional modification, using a phenylacrylamide as a model compound. We anticipate our approach can be expanded to identify novel reagents that form adducts with RNA and further explored to understand the relationship between RT processivity and natural post-transcriptional modifications in RNA.

Conformational heterogeneity of UCAAUC RNA oligonucleotide from molecular dynamics simulations, SAXS, and NMR experiments

We describe the conformational ensemble of the single-stranded r(UCAAUC) oligonucleotide obtained using extensive molecular dynamics (MD) simulations and Rosetta's FARFAR2 algorithm. The conformations observed in MD consist of A-form-like structures and variations thereof. These structures are not present in the pool generated using FARFAR2. By comparing with available nuclear magnetic resonance (NMR) measurements, we show that the presence of both A-form-like and other extended conformations is necessary to quantitatively explain experimental data. To further validate our results, we measure solution X-ray scattering (SAXS) data on the RNA hexamer and find that simulations result in more compact structures than observed from these experiments. The integration of simulations with NMR via a maximum entropy approach shows that small modifications to the MD ensemble lead to an improved description of the conformational ensemble. Nevertheless, we identify persisting discrepancies in matching experimental SAXS data.

Role of the Ribonuclease ONCONASE in miRNA Biogenesis and tRNA Processing: Focus on Cancer and Viral Infections

The majority of transcribed RNAs do not codify for proteins, nevertheless they display crucial regulatory functions by affecting the cellular protein expression profile. MicroRNAs (miRNAs) and transfer RNA-derived small RNAs (tsRNAs) are effectors of interfering mechanisms, so that their biogenesis is a tightly regulated process. Onconase (ONC) is an amphibian ribonuclease known for cytotoxicity against tumors and antiviral activity. Additionally, ONC administration in patients resulted in clinical effectiveness and in a well-tolerated feature, at least for lung carcinoma and malignant mesothelioma. Moreover, the ONC therapeutic effects are actually potentiated by cotreatment with many conventional antitumor drugs. This review not only aims to describe the ONC activity occurring either in different tumors or in viral infections but also to analyze the molecular mechanisms underlying ONC pleiotropic and cellular-specific effects. In cancer, data suggest that ONC affects malignant phenotypes by generating tRNA fragments and miRNAs able to downregulate oncogenes expression and upregulate tumor-suppressor proteins. In cells infected by viruses, ONC hampers viral spread by digesting the primer tRNAs necessary for viral DNA replication. In this scenario, new therapeutic tools might be developed by exploiting the action of ONC-elicited RNA derivatives.

RNA-Binding Macrocyclic Peptides

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

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

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

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.

Proteolysis-Targeting Chimera (PROTAC): Is the Technology Looking at the Treatment of Brain Tumors?

Post-translational modifications, such as ubiquitylation, need to be tightly controlled to guarantee the accurate localization and activity of proteins. Ubiquitylation is a dynamic process primarily responsible for proteasome-mediated degradation of substrate proteins and crucial for both normal homeostasis and disease. Alterations in ubiquitylation lead to the upregulation of oncoproteins and/or downregulation of tumor suppressors, thus concurring in tumorigenesis. PROteolysis-TArgeting Chimera (PROTAC) is an innovative strategy that takes advantage by the cell’s own Ubiquitin-Proteasome System (UPS). Each PROTAC molecule is composed by a ligand that recruits the target protein of interest (POI), a ligand specific for an E3 ubiquitin ligase enzyme, and a linker that connects these units. Upon binding to the POI, the PROTAC recruits the E3 inducing ubiquitylation-dependent proteasome degradation of the POI. To date, PROTAC technology has entered in clinical trials for several human cancers. Here, we will discuss the advantages and limitations of PROTACs development and safety considerations for their clinical application. Furthermore, we will review the potential of PROTAC strategy as therapeutic option in brain tumor, focusing on glioblastoma.