Targeting RNA structures in diseases with small molecules

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

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

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.

Non-coding RNAs in disease: from mechanisms to therapeutics

Non-coding RNAs (ncRNAs) are a heterogeneous group of transcripts that, by definition, are not translated into proteins. Since their discovery, ncRNAs have emerged as important regulators of multiple biological functions across a range of cell types and tissues, and their dysregulation has been implicated in disease. Notably, much research has focused on the link between microRNAs (miRNAs) and human cancers, although other ncRNAs, such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are also emerging as relevant contributors to human disease. In this Review, we summarize our current understanding of the roles of miRNAs, lncRNAs and circRNAs in cancer and other major human diseases, notably cardiovascular, neurological and infectious diseases. Further, we discuss the potential use of ncRNAs as biomarkers of disease and as therapeutic targets.

Therapeutic potential of alternative splicing in cardiovascular diseases

RNA splicing is an important RNA processing step required by multiexon protein-coding mRNAs and some noncoding RNAs. Precise RNA splicing is required for maintaining gene and cell function; however, mis-spliced RNA transcripts can lead to loss- or gain-of-function effects in human diseases. Mis-spliced RNAs induced by gene mutations or the dysregulation of splicing regulators may result in frameshifts, nonsense-mediated decay (NMD), or inclusion/exclusion of exons. Genetic animal models have characterised multiple splicing factors required for cardiac development or function. Moreover, sarcomeric and ion channel genes, which are closely associated with cardiovascular function and disease, are hotspots for AS. Here, we summarise splicing factors and their targets that are associated with cardiovascular diseases, introduce some therapies potentially related to pathological AS targets, and raise outstanding questions and future directions in this field.

Causes, functions, and therapeutic possibilities of RNA secondary structure ensembles and alternative states

RNA secondary structure plays essential roles in encoding RNA regulatory fate and function. Most RNAs populate ensembles of alternatively paired states and are continually unfolded and refolded by cellular processes. Measuring these structural ensembles and their contributions to cellular function has traditionally posed major challenges, but new methods and conceptual frameworks are beginning to fill this void. In this review, we provide a mechanism- and function-centric compendium of the roles of RNA secondary structural ensembles and minority states in regulating the RNA life cycle, from transcription to degradation. We further explore how dysregulation of RNA structural ensembles contributes to human disease and discuss the potential of drugging alternative RNA states to therapeutically modulate RNA activity. The emerging paradigm of RNA structural ensembles as central to RNA function provides a foundation for a deeper understanding of RNA biology and new therapeutic possibilities.

RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery

RNA structures, including those formed from coding and noncoding RNAs, alternative to protein-based drug targets, could be a promising target of small molecules for drug discovery against various human diseases, particularly in anticancer, antibacterial and antivirus development. The normal cellular activity of cells is critically dependent on the function of various RNA molecules generated from DNA transcription. Moreover, many studies support that mRNA-targeting small molecules may regulate the synthesis of disease-related proteins via the non-covalent mRNA-ligand interactions that do not involve gene modification. RNA-ligand interaction is thus an attractive approach to address the challenge of "undruggable" proteins in drug discovery because the intracellular activity of these proteins is hard to be suppressed with small molecule ligands. We selectively surveyed a specific area of RNA structure-selective small molecule ligands in fluorescence live cell imaging and drug discovery because the area was currently underexplored. This state-of-the-art review thus mainly focuses on the research published within the past three years and aims to provide the most recent information on this research area; hopefully, it could be complementary to the previously reported reviews and give new insights into the future development on RNA-specific small molecule ligands for live cell imaging and drug discovery.

X-ray Structure Characterization of the Selective Recognition of AT Base Pair Sequences

The rational design of small molecules that target specific DNA sequences is a promising strategy to modulate gene expression. This report focuses on a diamidinobenzimidazole compound, whose selective binding to the minor groove of AT DNA sequences holds broad significance in the molecular recognition of AT-rich human promoter sequences. The objective of this study is to provide a more detailed and systematized understanding, at an atomic level, of the molecular recognition mechanism of different AT-specific sequences by a rationally designed minor groove binder. The specialized method of X-ray crystallography was utilized to investigate how the sequence-dependent recognition properties in general, A-tract, and alternating AT sequences affect the binding of diamidinobenzimidazole in the DNA minor groove. While general and A-tract AT sequences give a narrower minor groove, the alternating AT sequences intrinsically have a wider minor groove which typically constricts upon binding. A strong and direct hydrogen bond between the N-H of the benzimidazole and an H-bond acceptor atom in the minor groove is essential for DNA recognition in all sequences described. In addition, the diamidine compound specifically utilizes an interfacial water molecule for its DNA binding. DNA complexes of AATT and AAAAAA recognition sites show that the diamidine compound can bind in two possible orientations with a preference for water-assisted hydrogen bonding at either cationic end. The complex structures of AAATTT, ATAT, ATATAT, and AAAA are bound in a singular orientation. Analysis of the helical parameters shows a minor groove expansion of about 1 Å across all the nonalternating DNA complexes. The results from this systematic approach will convey a greater understanding of the specific recognition of a diverse array of AT-rich sequences by small molecules and more insight into the design of small molecules with enhanced specificity to AT and mixed DNA sequences.

Alternative polyadenylation regulation in cardiac development and cardiovascular disease

Cleavage and polyadenylation of pre-mRNAs is a necessary step for gene expression and function. Majority of human genes exhibit multiple polyadenylation sites, which can be alternatively used to generate different mRNA isoforms from a single gene. Alternative polyadenylation (APA) of pre-mRNAs is important for the proteome and transcriptome landscape. APA is tightly regulated during development and contributes to tissue-specific gene regulation. Mis-regulation of APA is linked to a wide range of pathological conditions. APA-mediated gene regulation in the heart is emerging as a new area of research. Here, we will discuss the impact of APA on gene regulation during heart development and in cardiovascular diseases. First, we will briefly review how APA impacts gene regulation and discuss molecular mechanisms that control APA. Then, we will address APA regulation during heart development and its dysregulation in cardiovascular diseases. Finally, we will discuss pre-mRNA targeting strategies to correct aberrant APA patterns of essential genes for the treatment or prevention of cardiovascular diseases. The RNA field is blooming due to advancements in RNA-based technologies. RNA-based vaccines and therapies are becoming the new line of effective and safe approaches for the treatment and prevention of human diseases. Overall, this review will be influential for understanding gene regulation at the RNA level via APA in the heart and will help design RNA-based tools for the treatment of cardiovascular diseases in the future.

RLBind: a deep learning method to predict RNA-ligand binding sites

Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics compared with regular therapeutics targeting proteins. RNAs can provide many potential drug targets with diverse structures and functions. However, up to now, only a few methods have been proposed. Predicting RNA-small molecule binding sites still remains a big challenge. New computational model is required to better extract the features and predict RNA-small molecule binding sites more accurately. In this paper, a deep learning model, RLBind, was proposed to predict RNA-small molecule binding sites from sequence-dependent and structure-dependent properties by combining global RNA sequence channel and local neighbor nucleotides channel. To our best knowledge, this research was the first to develop a convolutional neural network for RNA-small molecule binding sites prediction. Furthermore, RLBind also can be used as a potential tool when the RNA experimental tertiary structure is not available. The experimental results show that RLBind outperforms other state-of-the-art methods in predicting binding sites. Therefore, our study demonstrates that the combination of global information for full-length sequences and local information for limited local neighbor nucleotides in RNAs can improve the model's predictive performance for binding sites prediction. All datasets and resource codes are available at https://github.com/KailiWang1/RLBind.

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

MOTIVATION

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 'undruggable' protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that have hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule (RNA-SM) recognition.

RESULTS

Here, we present Harnessing RIBOnucleic acid-Small molecule Structures (HARIBOSS), a curated collection of RNA-SM structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties and ultimately the development of in silico strategies to identify RNA-targeting small molecules.

AVAILABILITY AND IMPLEMENTATION

HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lessons Learned and Yet-to-Be Learned on the Importance of RNA Structure in SARS-CoV-2 Replication

SARS-CoV-2, the etiological agent responsible for the COVID-19 pandemic, is a member of the virus family Coronaviridae, known for relatively extensive (~30-kb) RNA genomes that not only encode for numerous proteins but are also capable of forming elaborate structures. As highlighted in this review, these structures perform critical functions in various steps of the viral life cycle, ultimately impacting pathogenesis and transmissibility. We examine these elements in the context of coronavirus evolutionary history and future directions for curbing the spread of SARS-CoV-2 and other potential human coronaviruses. While we focus on structures supported by a variety of biochemical, biophysical, and/or computational methods, we also touch here on recent evidence for novel structures in both protein-coding and noncoding regions of the genome, including an assessment of the potential role for RNA structure in the controversial finding of SARS-CoV-2 integration in “long COVID” patients. This review aims to serve as a consolidation of previous works on coronavirus and more recent investigation of SARS-CoV-2, emphasizing the need for improved understanding of the role of RNA structure in the evolution and adaptation of these human viruses.

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.

Designing strategies of small-molecule compounds for modulating non-coding RNAs in cancer therapy

Non-coding RNAs (ncRNAs) have been defined as a class of RNA molecules transcribed from the genome but not encoding proteins, such as microRNAs, long non-coding RNAs, Circular RNAs, and Piwi-interacting RNAs. Accumulating evidence has recently been revealing that ncRNAs become potential druggable targets for regulation of several small-molecule compounds, based on their complex spatial structures and biological functions in cancer therapy. Thus, in this review, we focus on summarizing some new emerging designing strategies, such as high-throughput screening approach, small-molecule microarray approach, structure-based designing approach, phenotypic screening approach, fragment-based designing approach, and pharmacological validation approach. Based on the above-mentioned approaches, a series of representative small-molecule compounds, including Bisphenol-A, Mitoxantrone and Enoxacin have been demonstrated to modulate or selectively target ncRNAs in different types of human cancers. Collectively, these inspiring findings would provide a clue on developing more novel avenues for pharmacological modulations of ncRNAs with small-molecule drugs for future cancer therapeutics.

Long non-coding RNAs as possible therapeutic targets in protozoa, and in Schistosoma and other helminths

Long non-coding RNAs (lncRNAs) emerged in the past 20 years due to massive amounts of scientific data regarding transcriptomic analyses. They have been implicated in a plethora of cellular processes in higher eukaryotes. However, little is known about lncRNA possible involvement in parasitic diseases, with most studies only detecting their presence in parasites of human medical importance. Here, we review the progress on lncRNA studies and their functions in protozoans and helminths. In addition, we show an example of knockdown of one lncRNA in Schistosoma mansoni, SmLINC156349, which led to in vitro parasite adhesion, motility, and pairing impairment, with a 20% decrease in parasite viability and 33% reduction in female oviposition. Other observed phenotypes were a decrease in the proliferation rate of both male and female worms and their gonads, and reduced female lipid and vitelline droplets that are markers for well-developed vitellaria. Impairment of female worms' vitellaria in SmLINC156349-silenced worms led to egg development deficiency. All those results demonstrate the great potential of the tools and methods to characterize lncRNAs as potential new therapeutic targets. Further, we discuss the challenges and limitations of current methods for studying lncRNAs in parasites and possible solutions to overcome them, and we highlight the future directions of this exciting field.

Docking and scoring for nucleic acid-ligand interactions: Principles and current status

Nucleic acid (NA)-ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein-ligand interactions might not be applicable to NA-ligand docking. Therefore, various sampling strategies and scoring functions for NA-ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA-ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

The SARS-CoV-2 Programmed -1 Ribosomal Frameshifting Element Crystal Structure Solved to 2.09 Å Using Chaperone-Assisted RNA Crystallography

The programmed -1 ribosomal frameshifting element (PFSE) of SARS-CoV-2 is a well conserved structured RNA found in all coronaviruses' genomes. By adopting a pseudoknot structure in the presence of the ribosome, the PFSE promotes a ribosomal frameshifting event near the stop codon of the first open reading frame Orf1a during translation of the polyprotein pp1a. Frameshifting results in continuation of pp1a via a new open reading frame, Orf1b, that produces the longer pp1ab polyprotein. Polyproteins pp1a and pp1ab produce nonstructural proteins NSPs 1-10 and NSPs 1-16, respectively, which contribute vital functions during the viral life cycle and must be present in the proper stoichiometry. Both drugs and sequence alterations that affect the stability of the -1 programmed ribosomal frameshifting element disrupt the stoichiometry of the NSPs produced, which compromise viral replication. For this reason, the -1 programmed frameshifting element is considered a promising drug target. Using chaperone assisted RNA crystallography, we successfully crystallized and solved the three-dimensional structure of the PFSE. We observe a three-stem H-type pseudoknot structure with the three stems stacked in a vertical orientation stabilized by two triple base pairs at the stem 1/stem 2 and stem 1/stem 3 junctions. This structure provides a new conformation of PFSE distinct from the bent conformations inferred from midresolution cryo-EM models and provides a high-resolution framework for mechanistic investigations and structure-based drug design.

An introduction to the themed issue on RNA biology in China

The year 2019 marked the fortieth anniversary of the Chinese Society of Biochemistry and Molecular Biology (CSBMB), whose mission is to promote biomolecular research and education in China. The last 40 years have witnessed tremendous growth and achievements in biomolecular research by Chinese scientists and Essays in Biochemistry is delighted to publish this themed issue that focuses on exciting areas within RNA biology, with each review contributed by key experts from China.

The Integral Role of RNA in Stress Granule Formation and Function

Stress granules (SGs) are phase-separated, membraneless, cytoplasmic ribonucleoprotein (RNP) assemblies whose primary function is to promote cell survival by condensing translationally stalled mRNAs, ribosomal components, translation initiation factors, and RNA-binding proteins (RBPs). While the protein composition and the function of proteins in the compartmentalization and the dynamics of assembly and disassembly of SGs has been a matter of study for several years, the role of RNA in these structures had remained largely unknown. RNA species are, however, not passive members of RNA granules in that RNA by itself can form homo and heterotypic interactions with other RNA molecules leading to phase separation and nucleation of RNA granules. RNA can also function as molecular scaffolds recruiting multivalent RBPs and their interactors to form higher-order structures. With the development of SG purification techniques coupled to RNA-seq, the transcriptomic landscape of SGs is becoming increasingly understood, revealing the enormous potential of RNA to guide the assembly and disassembly of these transient organelles. SGs are not only formed under acute stress conditions but also in response to different diseases such as viral infections, cancer, and neurodegeneration. Importantly, these granules are increasingly being recognized as potential precursors of pathological aggregates in neurodegenerative diseases. In this review, we examine the current evidence in support of RNA playing a significant role in the formation of SGs and explore the concept of SGs as therapeutic targets.