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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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Hasanzadeh A, Ebadati A, Saeedi S, Kamali B, Noori H, Jamei B, Hamblin MR, Liu Y, Karimi M. Nucleic acid-responsive smart systems for controlled cargo delivery. Biotechnol Adv 2024; 74:108393. [PMID: 38825215 DOI: 10.1016/j.biotechadv.2024.108393] [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: 08/21/2023] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
Stimulus-responsive delivery systems allow controlled, highly regulated, and efficient delivery of various cargos while minimizing side effects. Owing to the unique properties of nucleic acids, including the ability to adopt complex structures by base pairing, their easy synthesis, high specificity, shape memory, and configurability, they have been employed in autonomous molecular motors, logic circuits, reconfigurable nanoplatforms, and catalytic amplifiers. Moreover, the development of nucleic acid (NA)-responsive intelligent delivery vehicles is a rapidly growing field. These vehicles have attracted much attention in recent years due to their programmable, controllable, and reversible properties. In this work, we review several types of NA-responsive controlled delivery vehicles based on locks and keys, including DNA/RNA-responsive, aptamer-responsive, and CRISPR-responsive, and summarize their advantages and limitations.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Arefeh Ebadati
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran; Department of Molecular and Cell Biology, University of California, Merced, Merced, USA
| | - Sara Saeedi
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran; Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Babak Kamali
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Behnam Jamei
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
| | - Yong Liu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran; Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran; Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran, Iran.
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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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Peng HC, Castro GL, Karthikeyan V, Jarrett A, Katz MA, Hargrove JA, Hoang D, Hilber S, Meng W, Wang L, Fick RJ, Ahn JM, Kreutz C, Stelling AL. Measuring the Enthalpy of an Individual Hydrogen Bond in a DNA Duplex with Nucleobase Isotope Editing and Variable-Temperature Infrared Spectroscopy. J Phys Chem Lett 2023; 14:4313-4321. [PMID: 37130045 DOI: 10.1021/acs.jpclett.3c00178] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The level of interest in probing the strength of noncovalent interactions in DNA duplexes is high, as these weak forces dictate the range of suprastructures the double helix adopts under different conditions, in turn directly impacting the biological functions and industrial applications of duplexes that require making and breaking them to access the genetic code. However, few experimental tools can measure these weak forces embedded within large biological suprastructures in the native solution environment. Here, we develop experimental methods for detecting the presence of a single noncovalent interaction [a hydrogen bond (H-bond)] within a large DNA duplex in solution and measure its formation enthalpy (ΔHf). We report that introduction of a H-bond into the TC2═O group from the noncanonical nucleobase 2-aminopurine produces an expected decrease ∼10 ± 0.76 cm-1 (from ∼1720 cm-1 in Watson-Crick to ∼1710 cm-1 in 2-aminopurine), which correlates with an enthalpy of ∼0.93 ± 0.066 kcal/mol for this interaction.
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Affiliation(s)
- Hao-Che Peng
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Gabrielle L Castro
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Varshini Karthikeyan
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Alina Jarrett
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Melanie A Katz
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - James A Hargrove
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - David Hoang
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Stefan Hilber
- Institute of Organic Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck 6020, Austria
| | - Wenting Meng
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Lu Wang
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Robert J Fick
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Jung-Mo Ahn
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
| | - Christoph Kreutz
- Institute of Organic Chemistry and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck 6020, Austria
| | - Allison L Stelling
- Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, Texas 75080, United States
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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