1
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Zhou Y, Chen SJ. Advances in machine-learning approaches to RNA-targeted drug design. ARTIFICIAL INTELLIGENCE CHEMISTRY 2024; 2:100053. [PMID: 38434217 PMCID: PMC10904028 DOI: 10.1016/j.aichem.2024.100053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
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.
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Affiliation(s)
- Yuanzhe Zhou
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211-7010, USA
| | - Shi-Jie Chen
- Department of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA
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2
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Kallert E, Almena Rodriguez L, Husmann JÅ, Blatt K, Kersten C. Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system. RSC Med Chem 2024; 15:1527-1538. [PMID: 38784459 PMCID: PMC11110755 DOI: 10.1039/d3md00696d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/16/2024] [Indexed: 05/25/2024] Open
Abstract
Targeting RNA including viral RNAs with small molecules is an emerging field. The hepatitis C virus internal ribosome entry site (HCV IRES) is a potential target for translation inhibitor development to raise drug resistance mutation preparedness. Using RNA-focused and unbiased molecule libraries, a structure-based virtual screening (VS) by molecular docking and pharmacophore analysis was performed against the HCV IRES subdomain IIa. VS hits were validated by a microscale thermophoresis (MST) binding assay and a Förster resonance energy transfer (FRET) assay elucidating ligand-induced conformational changes. Ten hit molecules were identified with potencies in the high to medium micromolar range proving the suitability of structure-based virtual screenings against RNA-targets. Hit compounds from a 2-guanidino-quinazoline series, like the strongest binder, compound 8b with an EC50 of 61 μM, show low molecular weight, moderate lipophilicity and reduced basicity compared to previously reported IRES ligands. Therefore, it can be considered as a potential starting point for further optimization by chemical derivatization.
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Affiliation(s)
- Elisabeth Kallert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Staudingerweg 5 55128 Mainz Germany
| | - Laura Almena Rodriguez
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Staudingerweg 5 55128 Mainz Germany
| | - Jan-Åke Husmann
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Staudingerweg 5 55128 Mainz Germany
| | - Kathrin Blatt
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Staudingerweg 5 55128 Mainz Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Staudingerweg 5 55128 Mainz Germany
- Institute for Quantitative and Computational Biosciences, Johannes Gutenberg-University BioZentrum I, Hanns-Dieter-Hüsch-Weg 15 55128 Mainz Germany
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3
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Heel SV, Breuker K. Investigating the Intramolecular Competition of Different RNA Binding Motifs for Neomycin B by Native Top-Down Mass Spectrometry. Chempluschem 2024:e202400178. [PMID: 38758051 DOI: 10.1002/cplu.202400178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/13/2024] [Indexed: 05/18/2024]
Abstract
The ongoing search for small molecule drugs that target ribonucleic acids (RNA) is complicated by a limited understanding of the principles that govern RNA-small molecule interactions. Here we have used stoichiometry-resolved native top-down mass spectrometry (MS) to study the binding of neomycin B to small model hairpin RNAs, an unstructured RNA, and a viral RNA construct. For 15-22 nt model RNAs with hairpin structure, we found that neomycin B binding to hairpin loops relies on interactions with both the nucleobases and the 2'-OH groups, and that a simple 5' or 3' overhang can introduce an additional binding motif. For a 47 nt RNA construct derived from stem IA of the human immunodeficiency virus 1 (HIV-1) rev response element (RRE) RNA, native top-down MS identified four different binding motifs, of which the purine-rich internal loop showed the highest affinity for neomycin B. Stoichiometry-resolved binding site mapping by native top-down MS allows for a new perspective on binding specificity, and has the potential to reveal unexpected principles of small molecule binding to RNA.
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Affiliation(s)
- Sarah Viola Heel
- Institute of Organic Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
| | - Kathrin Breuker
- Institute of Organic Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020, Innsbruck, Austria
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4
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Min Y, Xiong W, Shen W, Liu X, Qi Q, Zhang Y, Fan R, Fu F, Xue H, Yang H, Sun X, Ning Y, Tian T, Zhou X. Developing nucleoside tailoring strategies against SARS-CoV-2 via ribonuclease targeting chimera. SCIENCE ADVANCES 2024; 10:eadl4393. [PMID: 38598625 PMCID: PMC11006213 DOI: 10.1126/sciadv.adl4393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/06/2024] [Indexed: 04/12/2024]
Abstract
In response to the urgent need for potent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) therapeutics, this study introduces an innovative nucleoside tailoring strategy leveraging ribonuclease targeting chimeras. By seamlessly integrating ribonuclease L recruiters into nucleosides, we address RNA recognition challenges and effectively inhibit severe acute respiratory syndrome coronavirus 2 replication in human cells. Notably, nucleosides tailored at the ribose 2'-position outperform those modified at the nucleobase. Our in vivo validation using hamster models further bolsters the promise of this nucleoside tailoring approach, positioning it as a valuable asset in the development of innovative antiviral drugs.
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Affiliation(s)
- Yuanqin Min
- Wuhan Institute of Virology; Hubei Jiangxia Laboratory; Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430200, Hubei, China
| | - Wei Xiong
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Wei Shen
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Xingyu Liu
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Qianqian Qi
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Yuanyuan Zhang
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Ruochen Fan
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Fang Fu
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Heng Xue
- Wuhan Institute of Virology; Hubei Jiangxia Laboratory; Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430200, Hubei, China
| | - Hang Yang
- Wuhan Institute of Virology; Hubei Jiangxia Laboratory; Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430200, Hubei, China
| | - Xiulian Sun
- Wuhan Institute of Virology; Hubei Jiangxia Laboratory; Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430200, Hubei, China
| | - Yunjia Ning
- Wuhan Institute of Virology; Hubei Jiangxia Laboratory; Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan 430200, Hubei, China
| | - Tian Tian
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
| | - Xiang Zhou
- Key Laboratory of Biomedical Polymers of Ministry of Education, College of Chemistry and Molecular Sciences, Hubei Province Key Laboratory of Allergy and Immunology, Wuhan University, Wuhan 430072, Hubei, China
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5
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Morishita EC, Nakamura S. Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery. Expert Opin Drug Discov 2024; 19:415-431. [PMID: 38321848 DOI: 10.1080/17460441.2024.2313455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality. AREAS COVERED The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs. EXPERT OPINION Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.
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6
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Kovachka S, Panosetti M, Grimaldi B, Azoulay S, Di Giorgio A, Duca M. Small molecule approaches to targeting RNA. Nat Rev Chem 2024; 8:120-135. [PMID: 38278932 DOI: 10.1038/s41570-023-00569-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
The development of innovative methodologies to identify RNA binders has attracted enormous attention in chemical biology and drug discovery. Although antibiotics targeting bacterial ribosomal RNA have been on the market for decades, the renewed interest in RNA targeting reflects the need to better understand complex intracellular processes involving RNA. In this context, small molecules are privileged tools used to explore the biological functions of RNA and to validate RNAs as therapeutic targets, and they eventually are to become new drugs. Despite recent progress, the rational design of specific RNA binders requires a better understanding of the interactions which occur with the RNA target to reach the desired biological response. In this Review, we discuss the challenges to approaching this underexplored chemical space, together with recent strategies to bind, interact and affect biologically relevant RNAs.
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Affiliation(s)
- Sandra Kovachka
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France
| | - Marc Panosetti
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France
- Molecular Medicine Research Line, Istituto Italiano di Tecnologia (IIT), Genoa, Italy
| | - Benedetto Grimaldi
- Molecular Medicine Research Line, Istituto Italiano di Tecnologia (IIT), Genoa, Italy
| | - Stéphane Azoulay
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France
| | - Audrey Di Giorgio
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France
| | - Maria Duca
- Université Côte d'Azur, CNRS, Institute of Chemistry of Nice, Nice, France.
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7
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Shino A, Otsu M, Imai K, Fukuzawa K, Morishita EC. Probing RNA-Small Molecule Interactions Using Biophysical and Computational Approaches. ACS Chem Biol 2023; 18:2368-2376. [PMID: 37856793 PMCID: PMC10662358 DOI: 10.1021/acschembio.3c00287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/30/2023] [Indexed: 10/21/2023]
Abstract
Interest in small molecules that target RNA is flourishing, and the expectation set on them to treat diseases with unmet medical needs is high. However, several challenges remain, including difficulties in selecting suitable tools and establishing workflows for their discovery. In this context, we optimized experimental and computational approaches that were previously employed for the protein targets. Here, we demonstrate that a fluorescence-based assay can be effectively used to screen small molecule libraries for their ability to bind and stabilize an RNA stem-loop. Our screen identified several fluoroquinolones that bind to the target stem-loop. We further probed their interactions with the target using biolayer interferometry, isothermal titration calorimetry (ITC), and nuclear magnetic resonance spectroscopy. The results of these biophysical assays suggest that the fluoroquinolones bind the target in a similar manner. Armed with this knowledge, we built models for the complexes of the fluoroquinolones and the RNA target. Then, we performed fragment molecular orbital (FMO) calculations to dissect the interactions between the fluoroquinolones and the RNA. We found that the binding free energies obtained from the ITC experiments correlated strongly with the interaction energies calculated by FMO. Finally, we designed fluoroquinolone analogues and performed FMO calculations to predict their binding free energies. Taken together, the results of this study support the importance of conducting orthogonal assays in binding confirmation and compound selection and demonstrate the usefulness of FMO calculations in the rational design of RNA-targeted small molecules.
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Affiliation(s)
- Amiu Shino
- Basic
Research Division, Veritas In Silico Inc., Shinagawa, Tokyo 141-0031, Japan
| | - Maina Otsu
- Basic
Research Division, Veritas In Silico Inc., Shinagawa, Tokyo 141-0031, Japan
| | - Koji Imai
- Basic
Research Division, Veritas In Silico Inc., Shinagawa, Tokyo 141-0031, Japan
| | - Kaori Fukuzawa
- Graduate
School of Pharmaceutical Sciences, Osaka
University, Suita, Osaka 565-0871, Japan
- School
of Pharmacy and Pharmaceutical Sciences, Hoshi University, Shinagawa, Tokyo 142-8501, Japan
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8
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Chan KH, Wang Y, Zheng BX, Long W, Feng X, Wong WL. RNA-Selective Small-Molecule Ligands: Recent Advances in Live-Cell Imaging and Drug Discovery. ChemMedChem 2023; 18:e202300271. [PMID: 37649155 DOI: 10.1002/cmdc.202300271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 08/13/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
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.
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Affiliation(s)
- Ka Hin Chan
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Yakun Wang
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, P. R. China
| | - Bo-Xin Zheng
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Wei Long
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
| | - Xinxin Feng
- State Key Laboratory of Chem-/Bio-Sensing and Chemometrics, Hunan Provincial Key Laboratory of Biomacromolecular Chemical Biology and School of Chemistry and Chemical Engineering, Hunan University, Changsha, Hunan, 410082, P. R. China
| | - Wing-Leung Wong
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, SAR 999077, P. R. China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, P. R. China
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9
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Fang L, Velema WA, Lee Y, Xiao L, Mohsen MG, Kietrys AM, Kool ET. Pervasive transcriptome interactions of protein-targeted drugs. Nat Chem 2023; 15:1374-1383. [PMID: 37653232 DOI: 10.1038/s41557-023-01309-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 07/27/2023] [Indexed: 09/02/2023]
Abstract
The off-target toxicity of drugs targeted to proteins imparts substantial health and economic costs. Proteome interaction studies can reveal off-target effects with unintended proteins; however, little attention has been paid to intracellular RNAs as potential off-targets that may contribute to toxicity. To begin to assess this, we developed a reactivity-based RNA profiling methodology and applied it to uncover transcriptome interactions of a set of Food and Drug Administration-approved small-molecule drugs in vivo. We show that these protein-targeted drugs pervasively interact with the human transcriptome and can exert unintended biological effects on RNA functions. In addition, we show that many off-target interactions occur at RNA loci associated with protein binding and structural changes, allowing us to generate hypotheses to infer the biological consequences of RNA off-target binding. The results suggest that rigorous characterization of drugs' transcriptome interactions may help assess target specificity and potentially avoid toxicity and clinical failures.
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Affiliation(s)
- Linglan Fang
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Willem A Velema
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Yujeong Lee
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Lu Xiao
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | | | - Anna M Kietrys
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Eric T Kool
- Department of Chemistry, Stanford University, Stanford, CA, USA.
- Sarafan ChEM-H Institute, Stanford University, Stanford, CA, USA.
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10
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Wicks SL, Morgan BS, Wilson AW, Hargrove AE. Probing Bioactive Chemical Space to Discover RNA-Targeted Small Molecules. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.31.551350. [PMID: 37577658 PMCID: PMC10418101 DOI: 10.1101/2023.07.31.551350] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Small molecules have become increasingly recognized as invaluable tools to study RNA structure and function and to develop RNA-targeted therapeutics. To rationally design RNA-targeting ligands, a comprehensive understanding and explicit testing of small molecule properties that govern molecular recognition is crucial. To date, most studies have primarily evaluated properties of small molecules that bind RNA in vitro, with little to no assessment of properties that are distinct to selective and bioactive RNA-targeted ligands. Therefore, we curated an RNA-focused library, termed the Duke RNA-Targeted Library (DRTL), that was biased towards the physicochemical and structural properties of biologically active and non-ribosomal RNA-targeted small molecules. The DRTL represents one of the largest academic RNA-focused small molecule libraries curated to date with more than 800 small molecules. These ligands were selected using computational approaches that measure similarity to known bioactive RNA ligands and that diversify the molecules within this space. We evaluated DRTL binding in vitro to a panel of four RNAs using two optimized fluorescent indicator displacement assays, and we successfully identified multiple small molecule hits, including several novel scaffolds for RNA. The DRTL has and will continue to provide insights into biologically relevant RNA chemical space, such as the identification of additional RNA-privileged scaffolds and validation of RNA-privileged molecular features. Future DRTL screening will focus on expanding both the targets and assays used, and we welcome collaboration from the scientific community. We envision that the DRTL will be a valuable resource for the discovery of RNA-targeted chemical probes and therapeutic leads.
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Affiliation(s)
- Sarah L. Wicks
- Department of Chemistry; Duke University; 124 Science Drive; Durham, NC 27708
| | - Brittany S. Morgan
- Department of Chemistry & Biochemistry; University of Notre Dame; 123 McCourtney Hall Notre Dame, IN 46556
| | - Alexander W. Wilson
- Department of Chemistry; Duke University; 124 Science Drive; Durham, NC 27708
| | - Amanda E. Hargrove
- Department of Chemistry; Duke University; 124 Science Drive; Durham, NC 27708
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11
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Roy S, Maiti B, Banerjee N, Kaulage MH, Muniyappa K, Chatterjee S, Bhattacharya S. New Xanthone Derivatives as Potent G-Quadruplex Binders for Developing Anti-Cancer Therapeutics. ACS Pharmacol Transl Sci 2023; 6:546-566. [PMID: 37082748 PMCID: PMC10111628 DOI: 10.1021/acsptsci.2c00205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Indexed: 04/22/2023]
Abstract
Xanthone is an important scaffold for various medicinally relevant compounds. However, it has received scant attention in the design of agents that are cytotoxic to cancer cells via targeting the stabilization of G-quadruplex (G4) nucleic acids. Specific G4 DNA recognition against double-stranded (ds) DNA is receiving epoch-making interest for the development of G4-mediated anticancer agents. Toward this goal, we have synthesized xanthone-based derivatives with various functionalized side-arm substituents that exhibited significant selectivity for G4 DNA as compared to dsDNA. The specific interaction has been demonstrated by performing various biophysical experiments. Based on the computational study as well as the competitive ligand binding assay, it is inferred that the potent compounds exhibit an end-stacking mode of binding with G4 DNA. Additionally, compound-induced conformational changes in the flanking nucleotides form the binding pocket for effective interaction. Selective action of the compounds on cancer cells suggests their effectiveness as potent anti-cancer agents. This study promotes the importance of structure-based screening approaches to get molecular insights for new scaffolds toward desired specific recognition of non-canonical G4 DNA structures.
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Affiliation(s)
- Soma Roy
- Department
of Organic Chemistry, Indian Institute of
Science, Bangalore 560012, India
- School
of Applied & Interdisciplinary Sciences, Indian Association for the Cultivation of Science, Kolkata 700032, India
| | - Bappa Maiti
- School
of Applied & Interdisciplinary Sciences, Indian Association for the Cultivation of Science, Kolkata 700032, India
| | - Nilanjan Banerjee
- Department
of Biophysics, Bose Institute, P-1/12 CIT Scheme VII (M), Kolkata 700054, India
| | - Mangesh H. Kaulage
- Department
of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Kalappa Muniyappa
- Department
of Biochemistry, Indian Institute of Science, Bangalore 560012, India
| | - Subhrangsu Chatterjee
- Department
of Biophysics, Bose Institute, P-1/12 CIT Scheme VII (M), Kolkata 700054, India
| | - Santanu Bhattacharya
- Department
of Organic Chemistry, Indian Institute of
Science, Bangalore 560012, India
- School
of Applied & Interdisciplinary Sciences, Indian Association for the Cultivation of Science, Kolkata 700032, India
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12
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Bagnolini G, Luu TB, Hargrove AE. Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA (NEW YORK, N.Y.) 2023; 29:473-488. [PMID: 36693763 PMCID: PMC10019373 DOI: 10.1261/rna.079497.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
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Affiliation(s)
- Greta Bagnolini
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - TinTin B Luu
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Amanda E Hargrove
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
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13
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Nickbarg EB, Spencer KB, Mortison JD, Lee JT. Targeting RNA with small molecules: lessons learned from Xist RNA. RNA (NEW YORK, N.Y.) 2023; 29:463-472. [PMID: 36725318 PMCID: PMC10019374 DOI: 10.1261/rna.079523.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Although more than 98% of the human genome is noncoding, nearly all drugs on the market target one of about 700 disease-related proteins. However, an increasing number of diseases are now being attributed to noncoding RNA and the ability to target them would vastly expand the chemical space for drug development. We recently devised a screening strategy based upon affinity-selection mass spectrometry and succeeded in identifying bioactive compounds for the noncoding RNA prototype, Xist. One such compound, termed X1, has drug-like properties and binds specifically to the RepA motif of Xist in vitro and in vivo. Small-angle X-ray scattering analysis reveals that X1 changes the conformation of RepA in solution, thereby explaining the displacement of cognate interacting protein factors (PRC2 and SPEN) and inhibition of X-chromosome inactivation. In this Perspective, we discuss lessons learned from these proof-of-concept experiments and suggest that RNA can be systematically targeted by drug-like compounds to disrupt RNA structure and function.
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Affiliation(s)
| | | | | | - Jeannie T Lee
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, USA
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RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study. Int J Mol Sci 2023; 24:ijms24065497. [PMID: 36982570 PMCID: PMC10058308 DOI: 10.3390/ijms24065497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
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.
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15
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Garner AL. Contemporary Progress and Opportunities in RNA-Targeted Drug Discovery. ACS Med Chem Lett 2023; 14:251-259. [PMID: 36923915 PMCID: PMC10009794 DOI: 10.1021/acsmedchemlett.3c00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
The surprising discovery that RNAs are the predominant gene products to emerge from the human genome catalyzed a renaissance in RNA biology. It is now well-understood that RNAs act as more than just a messenger and comprise a large and diverse family of ribonucleic acids of differing sizes, structures, and functions. RNAs play expansive roles in the cell, contributing to the regulation and fine-tuning of nearly all aspects of gene expression and genome architecture. In line with the significance of these functions, we have witnessed an explosion in discoveries connecting RNAs with a variety of human diseases. Consequently, the targeting of RNAs, and more broadly RNA biology, has emerged as an untapped area of drug discovery, making the search for RNA-targeted therapeutics of great interest. In this Microperspective, I highlight contemporary learnings in the field and present my views on how to catapult us toward the systematic discovery of RNA-targeted medicines.
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Affiliation(s)
- Amanda L. Garner
- Department of Medicinal Chemistry,
College of Pharmacy, University of Michigan, 1600 Huron Parkway, NCRC B520, Ann Arbor, Michigan 48109, United States
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16
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Yazdani K, Jordan D, Yang M, Fullenkamp CR, Calabrese DR, Boer R, Hilimire T, Allen TEH, Khan RT, Schneekloth JS. Machine Learning Informs RNA-Binding Chemical Space. Angew Chem Int Ed Engl 2023; 62:e202211358. [PMID: 36584293 PMCID: PMC9992102 DOI: 10.1002/anie.202211358] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 01/01/2023]
Abstract
Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.
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Affiliation(s)
- Kamyar Yazdani
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Deondre Jordan
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Mo Yang
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Christopher R. Fullenkamp
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - David R. Calabrese
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Robert Boer
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | - Thomas Hilimire
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
| | | | | | - John S. Schneekloth
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702-1201, USA
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17
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Morishita EC. Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies. Expert Opin Drug Discov 2023; 18:207-226. [PMID: 36322542 DOI: 10.1080/17460441.2022.2134852] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
INTRODUCTION The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approved risdiplam, the biopharmaceutical industry is gaining confidence in the field. This review, based on the literature obtained from PubMed, aims to disseminate information about the various technologies developed for targeting RNA with small molecules and propose areas for improvement to develop drugs more efficiently, particularly those linked to diseases with unmet medical needs. AREAS COVERED The technologies for the identification of RNA targets, screening of chemical libraries against RNA, assessing the bioactivity and target engagement of the hit compounds, structure determination, and hit-to-lead optimization are reviewed. Along with the description of the technologies, their strengths, limitations, and examples of how they can impact drug discovery are provided. EXPERT OPINION Many existing technologies employed for protein targets have been repurposed for use in the discovery of RNA-targeted small molecules. In addition, technologies tailored for RNA targets have been developed. Nevertheless, more improvements are necessary, such as artificial intelligence to dissect important RNA structures and RNA-small-molecule interactions and more powerful chemical probing and structure prediction techniques.
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18
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Kallert E, Fischer TR, Schneider S, Grimm M, Helm M, Kersten C. Protein-Based Virtual Screening Tools Applied for RNA-Ligand Docking Identify New Binders of the preQ 1-Riboswitch. J Chem Inf Model 2022; 62:4134-4148. [PMID: 35994617 DOI: 10.1021/acs.jcim.2c00751] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Targeting RNA with small molecules is an emerging field. While several ligands for different RNA targets are reported, structure-based virtual screenings (VSs) against RNAs are still rare. Here, we elucidated the general capabilities of protein-based docking programs to reproduce native binding modes of small-molecule RNA ligands and to discriminate known binders from decoys by the scoring function. The programs were found to perform similar compared to the RNA-based docking tool rDOCK, and the challenges faced during docking, namely, protomer and tautomer selection, target dynamics, and explicit solvent, do not largely differ from challenges in conventional protein-ligand docking. A prospective VS with the Bacillus subtilis preQ1-riboswitch aptamer domain performed with FRED, HYBRID, and FlexX followed by microscale thermophoresis assays identified six active compounds out of 23 tested VS hits with potencies between 29.5 nM and 11.0 μM. The hits were selected not solely based on their docking score but for resembling key interactions of the native ligand. Therefore, this study demonstrates the general feasibility to perform structure-based VSs against RNA targets, while at the same time it highlights pitfalls and their potential solutions when executing RNA-ligand docking.
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Affiliation(s)
- Elisabeth Kallert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Simon Schneider
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Maike Grimm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Mark Helm
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University Mainz, Staudingerweg 5, Mainz 55128, Germany
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19
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Guo P, Han D. Targeting Pathogenic DNA and RNA Repeats: A Conceptual Therapeutic Way for Repeat Expansion Diseases. Chemistry 2022; 28:e202201749. [PMID: 35727679 DOI: 10.1002/chem.202201749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Indexed: 11/06/2022]
Abstract
Expansions of short tandem repeats (STRs) in the human genome cause nearly 50 neurodegenerative diseases, which are mostly inheritable, nonpreventable and incurable, posing as a huge threat to human health. Non-B DNAs formed by STRs are thought to be structural intermediates that can cause repeat expansions. The subsequent transcripts harboring expanded RNA repeats can further induce cellular toxicity through forming specific structures. Direct targeting of these pathogenic DNA and RNA repeats has emerged as a new potential therapeutic strategy to cure repeat expansion diseases. In this conceptual review, we first introduce the roles of DNA and RNA structures in the genetic instabilities and pathomechanisms of repeat expansion diseases, then describe structural features of DNA and RNA repeats with a focus on the tertiary structures determined by X-ray crystallography and solution nuclear magnetic resonance spectroscopy, and finally discuss recent progress and perspectives of developing chemical tools that target pathogenic DNA and RNA repeats for curing repeat expansion diseases.
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Affiliation(s)
- Pei Guo
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P. R. China
| | - Da Han
- The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, P. R. China.,Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, School of Medicine Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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