Proximity-Induced Nucleic Acid Degrader (PINAD) Approach to Targeted RNA Degradation Using Small Molecules

ACS Central Science, Год журнала: 2023, Номер 9(5), С. 892 - 904

Опубликована: Апрель 26, 2023

Nature has evolved intricate machinery to target and degrade RNA, some of these molecular mechanisms can be adapted for therapeutic use. Small interfering RNAs RNase H-inducing oligonucleotides have yielded agents against diseases that cannot tackled using protein-centered approaches. Because are nucleic acid-based, they several inherent drawbacks which include poor cellular uptake stability. Here we report a new approach RNA small molecules, proximity-induced acid degrader (PINAD). We utilized this strategy design two families degraders different structures within the genome SARS-CoV-2: G-quadruplexes betacoronaviral pseudoknot. demonstrate novel molecules their targets in vitro, cellulo, vivo SARS-CoV-2 infection models. Our allows any binding molecule converted into degrader, empowering binders not potent enough exert phenotypic effect on own. PINAD raises possibility targeting destroying disease-related species, greatly expand space druggable diseases.

Язык: Английский

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

Briefings in Bioinformatics, Год журнала: 2023, Номер 24(4)

Опубликована: Май 25, 2023

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

Язык: Английский

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Pervasive transcriptome interactions of protein-targeted drugs

Nature Chemistry, Год журнала: 2023, Номер 15(10), С. 1374 - 1383

Опубликована: Авг. 31, 2023

Язык: Английский

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Contemporary Progress and Opportunities in RNA-Targeted Drug Discovery

ACS Medicinal Chemistry Letters, Год журнала: 2023, Номер 14(3), С. 251 - 259

Опубликована: Фев. 23, 2023

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

Язык: Английский

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

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(2)

Опубликована: Янв. 22, 2024

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

Язык: Английский

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RNATACs: Multispecific small molecules targeting RNA by induced proximity

Cell chemical biology, Год журнала: 2024, Номер 31(6), С. 1101 - 1117

Опубликована: Июнь 1, 2024

RNA-targeting small molecules (rSMs) have become an attractive modality to tackle traditionally undruggable proteins and expand the druggable space. Among many innovative concepts, chimeras (RNATACs) represent a new class of multispecific, induced proximity that act by chemically bringing RNA targets into with endogenous effector, such as ribonuclease (RNase). Depending on RNATACs can alter stability, localization, translation, or splicing target RNA. Although still in its infancy, this has potential for broad applications future treat diseases high unmet need. In review, we discuss advantages RNATACs, recent progress field, challenges cutting-edge technology.

Язык: Английский

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

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июль 8, 2024

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

Язык: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

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

Язык: Английский

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Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

Expert Opinion on Drug Discovery, Год журнала: 2024, Номер 19(4), С. 415 - 431

Опубликована: Фев. 6, 2024

Introduction Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet emerging medical needs. The recent rise of interest in strategy has already resulted large amounts data on disease associated RNAs, as well that bind such RNAs. Artificial intelligence (AI) approaches, including machine learning deep learning, present opportunity speed up RNA-targeted by improving decision-making efficiency quality.

Язык: Английский

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Discovery of Small Molecules Targeting the Frameshifting Element RNA in SARS-CoV-2 Viral Genome

ACS Medicinal Chemistry Letters, Год журнала: 2023, Номер 14(6), С. 757 - 765

Опубликована: Май 11, 2023

Targeting structured RNA elements in the SARS-CoV-2 viral genome with small molecules is an attractive strategy for pharmacological control over replication. In this work, we report discovery of that target frameshifting element (FSE) using high-throughput small-molecule microarray (SMM) screening. A new class aminoquinazoline ligands FSE are synthesized and characterized multiple orthogonal biophysical assays structure–activity relationship (SAR) studies. This work reveals compounds mid-micromolar binding affinity (KD = 60 ± 6 μM) to supports a mode distinct from previously reported binders MTDB merafloxacin. addition, active vitro dual-luciferase in-cell dual-fluorescent-reporter assays, highlighting promise targeting RNAs druglike alter expression proteins.

Язык: Английский

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