Understanding the Contributions of Conformational Changes, Thermodynamics, and Kinetics of RNA-Small Molecule Interactions

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

RNA-Binding Small Molecules in Drug Discovery and Delivery: An Overview from Fundamentals

RNA molecules, similar to proteins, fold into complex structures to confer diverse functions in cells. The intertwining of functions with RNA structures offers a new therapeutic opportunity for small molecules to bind and manipulate disease-relevant RNA pathways, thus creating a therapeutic realm of RNA-binding small molecules. The ongoing interest in RNA targeting and subsequent screening campaigns have led to the identification of numerous compounds that can regulate RNAs from splicing, degradation to malfunctions, with therapeutic benefits for a variety of diseases. Moreover, along with the rise of RNA-based therapeutics, RNA-binding small molecules have expanded their application to the modification, regulation, and delivery of RNA drugs, leading to the burgeoning interest in this field. This Perspective overviews the emerging roles of RNA-binding small molecules in drug discovery and delivery, covering aspects from their action fundamentals to therapeutic applications, which may inspire researchers to advance the field.

A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

Effects of mRNA conformational switching on translational noise in gene circuits

Intragenic translational heterogeneity describes the variation in translation at the level of transcripts for an individual gene. A factor that contributes to this source of variation is the mRNA structure. Both the composition of the thermodynamic ensemble, i.e., the stationary distribution of mRNA structures, and the switching dynamics between those play a role. The effect of the switching dynamics on intragenic translational heterogeneity remains poorly understood. We present a stochastic translation model that accounts for mRNA structure switching and is derived from a Markov model via approximate stochastic filtering. We assess the approximation on various timescales and provide a method to quantify how mRNA structure dynamics contributes to translational heterogeneity. With our approach, we allow quantitative information on mRNA switching from biophysical experiments or coarse-grain molecular dynamics simulations of mRNA structures to be included in gene regulatory chemical reaction network models without an increase in the number of species. Thereby, our model bridges a gap between mRNA structure kinetics and gene expression models, which we hope will further improve our understanding of gene regulatory networks and facilitate genetic circuit design.

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.

Photoswitchable molecular glue for RNA: reversible photocontrol of structure and function of the ribozyme

Single-stranded RNA folds into a variety of secondary and higher-order structures. Distributions and dynamics of multiple RNA conformations are responsible for the biological function of RNA. We here developed a photoswitchable molecular glue for RNA, which could reversibly control the association of two unpaired RNA regions in response to light stimuli. The photoswitchable molecular glue, NCTA, is an RNA-binding ligand possessing a photoisomerizable azobenzene moiety. Z-NCTA is an active ligand for the target RNA containing 5'-WGG-3'/5'-WGG-3' (W = U or A) site and stabilizes its hybridized state, while its isomer E-NCTA is not. Photoreversible isomerization of NCTA enabled control of the secondary and tertiary structure of the target RNA. The RNA-cleaving activity of hammerhead ribozyme, where appropriate RNA folding is necessary, could be reversibly regulated by photoirradiation in cells treated with NCTA, demonstrating precise photocontrol of RNA structure and function by the photoswitchable molecular glue.

Innovations in targeting RNA by fragment-based ligand discovery

A subset of functional regions within large RNAs fold into complex structures able to bind small-molecule ligands with high affinity and specificity. Fragment-based ligand discovery (FBLD) offers notable opportunities for discovery and design of potent small molecules that bind pockets in RNA. Here we share an integrated analysis of recent innovations in FBLD, emphasizing opportunities resulting from fragment elaboration via both linking and growing. Analysis of elaborated fragments emphasizes that high-quality interactions form with complex tertiary structures in RNA. FBLD-inspired small molecules have been shown to modulate RNA functions by competitively inhibiting protein binding and by selectively stabilizing dynamic RNA states. FBLD is creating a foundation to interrogate the relatively unknown structural space for RNA ligands and for discovery of RNA-targeted therapeutics.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

RNA-binding of Ru(II) complexes [Ru(phen)2(7-OCH3-dppz)]2+ and [Ru(phen)2(7-NO2-dppz)]2+: The former serves as a molecular "light switch" for poly(A)•poly(U)

To further determine the factors that affect the binding properties of ruthenium(II) polypyridine complexes with RNA duplex and to find excellent RNA-binding agents, the binding properties of ruthenium(II) complexes [Ru(phen)₂(7-OCH₃-dppz)]²⁺ (Ru1, phen = 1,10-phenan- throline, 7-OCH₃-dppz = 7-methoxy-dipyrido-[3,2-a,2',3'-c]-phenazine) and [Ru(phen)₂(7-NO₂- dppz)]²⁺ (Ru2, 7-NO₂-dppz = 7-nitro-dipyrido-[3,2-a,2',3'-c]-phenazine) with RNA poly(A)•poly(U) duplex have been investigated by spectroscopic methods and viscosity measurements in this work. The results show that complexes Ru1 and Ru2 bind to poly(A)•poly(U) through intercalation and the binding affinity between Ru2 and poly(A)•poly(U) is greater than that of Ru1. Thermal denaturation experiments suggest that both ruthenium(II) complexes exhibit a significant stabilizing effect on poly(A)•poly(U) duplex. Moreover, fluorescence emission spectra exhibit that, deviating from Ru2, Ru1 exhibits a "light switch" effect for poly(A)•poly(U). This effect can be observed by the naked eye under UV light and adjusted by pH, meaning that Ru1 may act as a reversible pH controlled molecular "light switch". The results obtained in this work will contribute to our understanding of the significant influence of the intercalative ligand substituent effect in the binding process of ruthenium(II) complexes with RNA duplex.

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.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

A drug discovery toolbox for Nuclear Magnetic Resonance (NMR) characterization of ligands and their targets

Information about the structure, dynamics, and ligand-binding properties of biomolecules can be derived from Nuclear Magnetic Resonance (NMR) spectroscopy and provides valuable information for drug discovery. A multitude of experimental approaches provides a wealth of information that can be tailored to the system of interest. Methods to study the behavior of ligands upon target binding enable the identification of weak binders in a robust manner that is critical for the identification of truly novel binding interactions. This is particularly important for challenging targets. Observing the solution behavior of biomolecules yields information about their structure, dynamics, and interactions. This review describes the breadth of approaches that are available, many of which are under-utilized in a drug-discovery environment, and focuses on recent advances that continue to emerge.

Role of salt-bridging interactions in recognition of viral RNA by arginine-rich peptides

Interactions between RNA molecules and proteins are critical to many cellular processes and are implicated in various diseases. The RNA-peptide complexes are good model systems to probe the recognition mechanism of RNA by proteins. In this work, we report studies on the binding-unbinding process of a helical peptide from a viral RNA element using nonequilibrium molecular dynamics simulations. We explored the existence of various dissociation pathways with distinct free-energy profiles that reveal metastable states and distinct barriers to peptide dissociation. We also report the free-energy differences for each of the four pathways to be 96.47 ± 12.63, 96.1 ± 10.95, 91.83 ± 9.81, and 92 ± 11.32 kcal/mol. Based on the free-energy analysis, we further propose the preferred pathway and the mechanism of peptide dissociation. The preferred pathway is characterized by the formation of sequential hydrogen-bonding and salt-bridging interactions between several key arginine amino acids and the viral RNA nucleotides. Specifically, we identified one arginine amino acid (R8) of the peptide to play a significant role in the recognition mechanism of the peptide by the viral RNA molecule.

Role of conformational heterogeneity in ligand recognition by viral RNA molecules

Ribonucleic acid (RNA) molecules are known to undergo conformational changes in response to various environmental stimuli including temperature, pH, and ligands. In particular, viral RNA molecules are a key example of conformationally adapting molecules that have evolved to switch between many functional conformations. The transactivation response element (TAR) RNA from the type-1 human immunodeficiency virus (HIV-1) is a viral RNA molecule that is being increasingly explored as a potential therapeutic target due to its role in the viral replication process. In this work, we have studied the dynamics in TAR RNA in apo and liganded states by performing explicit-solvent molecular dynamics (MD) simulations initiated with 27 distinct structures. We determined that the TAR RNA structure is significantly stabilized on ligand binding with especially decreased fluctuations in its two helices. This rigidity is further coupled with the decreased flipping of bulge nucleotides, which were observed to flip more frequently in the absence of ligands. We found that initially-distinct structures of TAR RNA converged to similar conformations on removing ligands. We also report that conformational dynamics in unliganded TAR structures leads to the formation of binding pockets capable of accommodating ligands of various sizes.

Screening strategies for identifying RNA- and ribonucleoprotein-targeted compounds

The past few years have witnessed important breakthroughs in the identification of compounds that specifically bind and regulate RNAs and in optimizing them for therapeutic use. Here, we review successful and unsuccessful approaches in screening for RNA-targeted small molecules. We discuss advantages and disadvantages of the different screening techniques and variables that affect the outcome of RNA-screening projects. We also highlight key challenges that hamper the development of quality RNA ligands, especially the still-low availability of RNA-specific compound libraries and the poor understanding of RNA structural dynamics. We conclude that the development of new RNA-targeting drugs would greatly benefit from integration of the power of high-throughput screening technologies with improved biochemical, structural, and computational characterization of RNA targets.

Role and Perspective of Molecular Simulation-Based Investigation of RNA-Ligand Interaction: From Small Molecules and Peptides to Photoswitchable RNA Binding

Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy.

What defines a synthetic riboswitch? - Conformational dynamics of ciprofloxacin aptamers with similar binding affinities but varying regulatory potentials

Among the many in vitro-selected aptamers derived from SELEX protocols, only a small fraction has the potential to be applied for synthetic riboswitch engineering. Here, we present a comparative study of the binding properties of three different aptamers that bind to ciprofloxacin with similar K_D values, yet only two of them can be applied as riboswitches. We used the inherent ligand fluorescence that is quenched upon binding as the reporter signal in fluorescence titration and in time-resolved stopped-flow experiments. Thus, we were able to demonstrate differences in the binding kinetics of regulating and non-regulating aptamers. All aptamers studied underwent a two-step binding mechanism that suggests an initial association step followed by a reorganization of the aptamer to accommodate the ligand. We show that increasing regulatory potential is correlated with a decreasing back-reaction rate of the second binding step, thus resulting in a virtually irreversible last binding step of regulating aptamers. We suggest that a highly favoured structural adaption of the RNA to the ligand during the final binding step is essential for turning an aptamer into a riboswitch. In addition, our results provide an explanation for the fact that so few aptamers with regulating capacity have been found to date. Based on our data, we propose an adjustment of the selection protocol for efficient riboswitch detection.

Targeting RNA with small molecules: from fundamental principles towards the clinic

Recent advances in our understanding of RNA biology have uncovered crucial roles for RNA in multiple disease states, ranging from viral and bacterial infections to cancer and neurological disorders. As a result, multiple laboratories have become interested in developing drug-like small molecules to target RNA. However, this development comes with multiple unique challenges. For example, RNA is inherently dynamic and has limited chemical diversity. In addition, promiscuous RNA-binding ligands are often identified during screening campaigns. This Tutorial Review overviews important considerations and advancements for generating RNA-targeted small molecules, ranging from fundamental chemistry to promising small molecule examples with demonstrated clinical efficacy. Specifically, we begin by exploring RNA functional classes, structural hierarchy, and dynamics. We then discuss fundamental RNA recognition principles along with methods for small molecule screening and RNA structure determination. Finally, we review unique challenges and emerging solutions from both the RNA and small molecule perspectives for generating RNA-targeted ligands before highlighting a selection of the "Greatest Hits" to date. These molecules target RNA in a variety of diseases, including cancer, neurodegeneration, and viral infection, in cellular and animal model systems. Additionally, we explore the recently FDA-approved small molecule regulator of RNA splicing, risdiplam, for treatment of spinal muscular atrophy. Together, this Tutorial Review showcases the fundamental role of chemical and molecular recognition principles in enhancing our understanding of RNA biology and contributing to the rapidly growing number of RNA-targeted probes and therapeutics. In particular, we hope this widely accessible review will serve as inspiration for aspiring small molecule and/or RNA researchers.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

RNA sequence and ligand binding alter conformational profile of SARS-CoV-2 stem loop II motif

Antiviral drug discovery continues to be an essential complement to vaccine development for overcoming the global pandemic caused by SARS-CoV-2. The genomic RNA of SARS-CoV-2 contains structural elements important for viral replication and/or pathogenesis making them potential therapeutic targets. Here we report on the stem-loop II motif, a highly conserved noncoding RNA element. Based on our homology model we determined that the G to U transversion in the SARS-CoV-2 stem-loop II motif (S2MG35U) forms a C–U base-pair isosteric to the C-G base-pair in the early 2000’s SARS-CoV (S2M). In addition, chemo-enzymatic probing and molecular dynamics simulations indicate the S2MG35U conformational profile is altered compared to S2M in the apical loop region. We explored S2MG35U as a potential drug target by docking a library of FDA approved drugs. Enzymatic probing of the best docking ligands (aminoglycosides and polymyxins) indicated that polymyxin binding alters the conformational profile and/or secondary structure of the RNA. The SARS-CoV-2 stem-loop II motif conformational differences due to nucleotide transversion and ligand binding are highly significant and provide insight for future drug discovery efforts since the conformation of noncoding RNA elements affects their function.

Frameworks for targeting RNA with small molecules

Since the characterization of mRNA in 1961, our understanding of the roles of RNA molecules has significantly grown. Beyond serving as a link between DNA and proteins, RNA molecules play direct effector roles by binding to various ligands, including proteins, DNA, other RNAs, and metabolites. Through these interactions, RNAs mediate cellular processes such as the regulation of gene transcription and the enhancement or inhibition of protein activity. As a result, the misregulation of RNA molecules is often associated with disease phenotypes, and RNA molecules have been increasingly recognized as potential targets for drug development efforts, which in the past had focused primarily on proteins. Although both small molecule-based and oligonucleotide-based therapies have been pursued in efforts to target RNA, small-molecule modalities are often favored owing to several advantages including greater oral bioavailability. In this review, we discuss three general frameworks (sets of premises and hypotheses) that, in our view, have so far dominated the discovery of small-molecule ligands for RNA. We highlight the unique merits of each framework as well as the pitfalls associated with exclusive focus of ligand discovery efforts within only one framework. Finally, we propose that RNA ligand discovery can benefit from using progress made within these three frameworks to move toward a paradigm that formulates RNA-targeting questions at the level of RNA structural subclasses.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Ligand Recognition in Viral RNA Necessitates Rare Conformational Transitions

Ribonucleic acids (RNAs) are conformationally flexible molecules that fold into three-dimensional structures and play an important role in different cellular processes as well as in the development of many diseases. RNA has therefore become an important target for developing novel therapeutic approaches. The biophysical processes underlying RNA function are often associated with rare structural transitions that play a key role in ligand recognition. In this work, we probe these rarely occurring transitions using nonequilibrium simulations by characterizing the dissociation of a ligand molecule from an HIV-1 viral RNA element. Specifically, we observed base-flipping rare events that are coupled with ligand binding/unbinding and also provided mechanistic details underlying these transitions.

Systematic analysis of the interactions driving small molecule-RNA recognition

RNA molecules are becoming an important target class in drug discovery. However, the principles for designing RNA-binding small molecules are yet to be fully uncovered. In this study, we examined the Protein Data Bank (PDB) to highlight privileged interactions underlying small molecule-RNA recognition. By comparing this analysis with previously determined small molecule-protein interactions, we find that RNA recognition is driven mostly by stacking and hydrogen bonding interactions, while protein recognition is instead driven by hydrophobic effects. Furthermore, we analyze patterns of interactions to highlight potential strategies to tune RNA recognition, such as stacking and cation-π interactions that favor purine and guanine recognition, and note an unexpected paucity of backbone interactions, even for cationic ligands. Collectively, this work provides further understanding of RNA-small molecule interactions that may inform the design of small molecules targeting RNA.

Assessing the feasibility and stability of uracil base flipping in RNA–small molecule complexes using molecular dynamics simulations

Small molecules can be used to target RNAs that mediate disease. A fundamental understanding of binding interactions between RNA and small molecules and the structure of their complexes will further inform the design of new targeting agents. Two small molecule ligands were investigated for their ability to recognize the expanded CUG repeat sequence in RNA, the causative agent of myotonic dystrophy type 1. We report the use of molecular dynamics simulations to explore small molecule–RNA complexes and the finding of a stabilized base flipped conformation at UU mismatches. The results of this computational study support experimental observations and suggest that base flipping is feasible for CUG-repeat RNA.

Extending the σ-Hole Motif for Sequence-Specific Recognition of the DNA Minor Groove

The majority of current drugs against diseases, such as cancer, can bind to one or more sites in a protein and inhibit its activity. There are, however, well-known limits on the number of druggable proteins, and complementary current drugs with compounds that could selectively target DNA or RNA would greatly enhance the availability of cellular probes and therapeutic progress. We are focusing on the design of sequence-specific DNA minor groove binders that, for example, target the promoter sites of transcription factors involved in a disease. We have started with AT-specific minor groove binders that are known to enter human cells and have entered clinical trials. To broaden the sequence-specific recognition of these compounds, several modules that have H-bond acceptors that strongly and specifically recognize G·C base pairs were identified. A lead module is a thiophene-N-alkyl-benzimidazole σ-hole-based system with terminal phenyl-amidines that have excellent affinity and selectivity for a G·C base pair in the minor groove. Efforts are now focused on optimizing this module. In this work, we are evaluating modifications to the compound aromatic system with the goal of improving GC selectivity and affinity. The lead compounds retain the thiophene-N-alkyl-BI module but have halogen substituents adjacent to an amidine group on the terminal phenyl-amidine. The optimum compounds must have strong affinity and specificity with a residence time of at least 100 s.

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

Although the potential value of RNA as a target for new small molecule therapeutics is becoming increasingly credible, the physicochemical properties required for small molecules to selectively bind to RNA remain relatively unexplored. To investigate the druggability of RNAs with small molecules, we have employed affinity mass spectrometry, using the Automated Ligand Identification System (ALIS), to screen 42 RNAs from a variety of RNA classes, each against an array of chemically diverse drug-like small molecules (~50,000 compounds) and functionally annotated tool compounds (~5100 compounds). The set of RNA–small molecule interactions that was generated was compared with that for protein–small molecule interactions, and naïve Bayesian models were constructed to determine the types of specific chemical properties that bias small molecules toward binding to RNA. This set of RNA-selective chemical features was then used to build an RNA-focused set of ~3800 small molecules that demonstrated increased propensity toward binding the RNA target set. In addition, the data provide an overview of the specific physicochemical properties that help to enable binding to potential RNA targets. This work has increased the understanding of the chemical properties that are involved in small molecule binding to RNA, and the methodology used here is generally applicable to RNA-focused drug discovery efforts.