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

Design, synthesis and characterization of aryl bis-guanyl hydrazones as RNA binders of C9orf72 G4C2 extended repeats

Expanded G4C2 repeats derived from mutations of the C9orf72 gene are causative factors in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) patients, leading to multiple pathological events. Bis thiophene para dinicotinimidamide 2a was reported to preferentially stabilize G-quadruplex G4C2 RNA structures at sub-micromolar concentrations. We replaced its amidine groups with BBB-compliant guanyl hydrazones, and carried out scaffold variations to improve water solubility. An eight-membered array was built around bis-thiophene- (4b-6a), bis-oxazole- (7b), diphenylurea diamide- (8b) and phenyldioxy ditriazolephenyl scaffolds (9a,b). Biological profiling of the array identified 4b as a promising, drug-like hit, active in cellular assays on ALS patient-derived cells.

Computational Hit Finding: An Industry Perspective

Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converged around mature software programs and stock chemical libraries up to ∼10 million in size. Recently, however, powered by massive increases in computational power, the emergence of ultralarge make-on-demand virtual libraries, the development of large capacity neural networks, the expansion of the domain of applicability of free energy calculations, and advances in protein structure prediction, the virtual screening field is currently seeing major change. We present a guide from industry practitioners summarizing key aspects on the changing computational hit finding landscape including practical recommendations for building a performant end-to-end screening workflow, strategies to mitigate risk by avoiding common pitfalls, determining success criteria, and a brief discussion of emerging technologies likely to impact drug discovery in the near future.

Ligand-binding pockets in RNA and where to find them

RNAs are critical regulators of gene expression, and their functions are often mediated by complex secondary and tertiary structures. Structured regions in RNA can selectively interact with small molecules-via well-defined ligand-binding pockets-to modulate the regulatory repertoire of an RNA. The broad potential to modulate biological function intentionally via RNA-ligand interactions remains unrealized, however, due to challenges in identifying compact RNA motifs with the ability to bind ligands with good physicochemical properties (often termed drug-like). Here, we devise fpocketR, a computational strategy that accurately detects pockets capable of binding drug-like ligands in RNA structures. Remarkably few, roughly 50, of such pockets have ever been visualized. We experimentally confirmed the ligandability of novel pockets detected with fpocketR using a fragment-based approach introduced here, Frag-MaP, that detects ligand-binding sites in cells. Analysis of pockets detected by fpocketR and validated by Frag-MaP reveals dozens of sites able to bind drug-like ligands, supports a model for RNA secondary structural motifs able to bind quality ligands, and creates a broad framework for understanding the RNA ligand-ome.

HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways

Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery. While existing drug-target databases have contributed valuable resources, they exhibit structural and functional fragmentation due to heterogeneous data sources and annotation standards. Building upon the high-confidence drug-gene interactions curated in HCDT 1.0, we present HCDT 2.0, a comprehensive and standardized resource that expands the scope through multiomics data integration. This update incorporates three-dimensional interactions including drug-gene, drug-RNA and drug-pathway interactions. The current version contains 1,284,353 curated interactions: 1,224,774 drug-gene pairs (678,564 drugs × 5,692 genes), 11,770 drug-RNA mappings (316 drugs × 6,430 RNAs), and 47,809 drug-pathway links (6,290 drugs × 3,143 pathways), alongside 16,317 drug-disease associations. To enhance biological interpretability, we further integrated pathway-gene and RNA-gene regulatory relationships. In addition, we integrated 38,653 negative DTIs covering 26,989 drugs and 1,575 genes. This integrative framework not only addresses critical gaps in cross-scale data representation but also establishes a robust foundation for systems pharmacology applications, including drug repurposing, adverse event prediction, and precision oncology strategies.

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models

RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule binding sites. However, accurate and efficient computational methods for identifying these interactions are still lacking. Recently, advances in large language models (LLMs), previously successful in DNA and protein research, have spurred the development of RNA-specific LLMs. These models leverage vast unlabeled RNA sequences to autonomously learn semantic representations with the goal of enhancing downstream tasks, particularly those constrained by limited annotated data. Here, we develop RNABind, an embedding-informed geometric deep learning framework to detect RNA-small molecule binding sites from RNA structures. RNABind integrates RNA LLMs into advanced geometric deep learning networks, which encodes both RNA sequence and structure information. To evaluate RNABind, we first compile the largest RNA-small molecule interaction dataset from the entire multi-chain complex structure instead of single-chain RNAs. Extensive experiments demonstrate that RNABind outperforms existing state-of-the-art methods. Besides, we conduct an extensive experimental evaluation of eight pre-trained RNA LLMs, assessing their performance on the binding site prediction task within a unified experimental protocol. In summary, RNABind provides a powerful tool on exploring RNA-small molecule binding site prediction, which paves the way for future innovations in the RNA-targeted drug discovery.

The evolution and application of RNA-focused small molecule libraries

RNA structure plays a role in nearly every disease. Therefore, approaches that identify tractable small molecule chemical matter that targets RNA and affects its function would transform drug discovery. Despite this potential, discovery of RNA-targeted small molecule chemical probes and medicines remains in its infancy. Advances in RNA-focused libraries are key to enable more successful primary screens and to define structure-activity relationships amongst hit molecules. In this review, we describe how RNA-focused small molecule libraries have been used and evolved over time and provide underlying principles for their application to develop bioactive small molecules. We also describe areas that need further investigation to advance the field, including generation of larger data sets to inform machine learning approaches.

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning

Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. With advancements in computer science and the availability of extensive biological data, deep learning methods have shown great promise in this area, particularly in efficiently predicting RNA-small molecule binding sites. However, few computational methods have been developed to predict RNA-small molecule binding affinities. Meanwhile, most of these approaches rely primarily on sequence or structural representations. Molecular surface information, vital for RNA and small molecule interactions, has been largely overlooked. To address these gaps, we propose a geometric deep learning method for predicting RNA-small molecule binding affinity, named RNA-ligand Surface Interaction Fingerprinting (RLASIF). In this study, we create RNA-ligand interaction fingerprints from the geometrical and chemical features present on molecular surface to characterize binding affinity. RLASIF outperformed other computational methods across ten different test sets from PDBbind NL2020. Compared to the second-best method, our approach improves performance by 10.01 %, 6.67 %, 2.01 % and 1.70 % on four evaluation metrics, indicating its effectiveness in capturing key features influencing RNA-ligand binding strength. Additionally, RLASIF holds potential for virtual screening of potential ligands for RNA and predicting small molecule binding nucleotides within RNA structures.

fpocketR: A platform for identification and analysis of ligand-binding pockets in RNA

Small molecules that bind specific sites in RNAs hold promise for altering RNA function, manipulating gene expression, and expanding the scope of druggable targets beyond proteins. Identifying binding sites in RNA that can engage ligands with good physicochemical properties remains a significant challenge. fpocketR is a software package for identifying, characterizing, and visualizing ligand-binding sites in RNA. fpocketR was optimized, through comprehensive analysis of currently available RNA-ligand complexes, to identify pockets in RNAs able to bind small molecules possessing favorable properties, generally termed drug-like. Here, we demonstrate use of fpocketR to analyze RNA-ligand interactions and novel pockets in small and large RNAs, to assess ensembles of RNA structure models, and to identify pockets in dynamic RNA systems. fpocketR performs best with RNA structures visualized at high (≤3.5 Å) resolution, but also provides useful information with lower resolution structures and computational models. fpocketR is a powerful, freely available tool for discovery and analysis of ligand-binding pockets in RNA molecules.

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning

RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale with large compound libraries and RNA targets. Machine learning offers a solution but remains underdeveloped for RNA due to limited data and practical evaluations. We introduce a data-driven VS pipeline tailored for RNA, utilizing coarse-grained 3D modeling, synthetic data augmentation, and RNA-specific self-supervision. Our model achieves a 10,000x speedup over docking while ranking active compounds in the top 2.8% on structurally distinct test sets. It is robust to binding site variations and successfully screens unseen RNA riboswitches in a 20,000-compound in-vitro microarray, with a mean enrichment factor of 2.93 at 1%. This marks the first experimentally validated success of structure-based deep learning for RNA VS.

Ligand-binding pockets in RNA, and where to find them

RNAs are critical regulators of gene expression, and their functions are often mediated by complex secondary and tertiary structures. Structured regions in RNA can selectively interact with small molecules - via well-defined ligand binding pockets - to modulate the regulatory repertoire of an RNA. The broad potential to modulate biological function intentionally via RNA-ligand interactions remains unrealized, however, due to challenges in identifying compact RNA motifs with the ability to bind ligands with good physicochemical properties (often termed drug-like). Here, we devise fpocketR, a computational strategy that accurately detects pockets capable of binding drug-like ligands in RNA structures. Remarkably few, roughly 50, of such pockets have ever been visualized. We experimentally confirmed the ligandability of novel pockets detected with fpocketR using a fragment-based approach introduced here, Frag-MaP, that detects ligand-binding sites in cells. Analysis of pockets detected by fpocketR and validated by Frag-MaP reveals dozens of newly identified sites able to bind drug-like ligands, supports a model for RNA secondary structural motifs able to bind quality ligands, and creates a broad framework for understanding the RNA ligand-ome.

Advances and Mechanisms of RNA-Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTP riboswitch ligands

The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure-informed design, crystallography, and machine learning-augmented all-atom molecular dynamics simulations (MD) we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the ZTP riboswitch, a ligand-binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on-rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics simulations and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identifies all the experimentally resolved pockets and ranks them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

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.

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.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

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.