1
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Yuce M, Ates B, Yasar NI, Sungur FA, Kurkcuoglu O. A computational workflow to determine drug candidates alternative to aminoglycosides targeting the decoding center of E. coli ribosome. J Mol Graph Model 2024; 131:108817. [PMID: 38976944 DOI: 10.1016/j.jmgm.2024.108817] [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: 03/22/2024] [Revised: 05/08/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.
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Affiliation(s)
- Merve Yuce
- Istanbul Technical University, Department of Chemical Engineering, Istanbul, 34469, Turkey.
| | - Beril Ates
- Istanbul Technical University, Department of Chemical Engineering, Istanbul, 34469, Turkey.
| | - Nesrin Isil Yasar
- Istanbul Technical University, Computational Science and Engineering Division, Informatics Institute, Istanbul, 34469, Turkey.
| | - Fethiye Aylin Sungur
- Istanbul Technical University, Computational Science and Engineering Division, Informatics Institute, Istanbul, 34469, Turkey.
| | - Ozge Kurkcuoglu
- Istanbul Technical University, Department of Chemical Engineering, Istanbul, 34469, Turkey.
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2
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Avila Y, Rebolledo LP, Skelly E, de Freitas Saito R, Wei H, Lilley D, Stanley RE, Hou YM, Yang H, Sztuba-Solinska J, Chen SJ, Dokholyan NV, Tan C, Li SK, He X, Zhang X, Miles W, Franco E, Binzel DW, Guo P, Afonin KA. Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology. ACS APPLIED BIO MATERIALS 2024; 7:3587-3604. [PMID: 38833534 PMCID: PMC11190997 DOI: 10.1021/acsabm.4c00432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/21/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024]
Abstract
Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.
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Affiliation(s)
- Yelixza
I. Avila
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Laura P. Rebolledo
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Elizabeth Skelly
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | - Renata de Freitas Saito
- Comprehensive
Center for Precision Oncology, Centro de Investigação
Translacional em Oncologia (LIM24), Departamento
de Radiologia e Oncologia, Faculdade de Medicina da Universidade de
São Paulo and Instituto do Câncer do Estado de São
Paulo, São Paulo, São Paulo 01246-903, Brazil
| | - Hui Wei
- College
of Engineering and Applied Sciences, Nanjing
University, Nanjing, Jiangsu 210023, P. R. China
| | - David Lilley
- School
of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom
| | - Robin E. Stanley
- Signal
Transduction Laboratory, National Institute of Environmental Health
Sciences, National Institutes of Health, Department of Health and Human Services, 111 T. W. Alexander Drive, Research Triangle Park, North Carolina 27709, United States
| | - Ya-Ming Hou
- Thomas
Jefferson
University, Department of Biochemistry
and Molecular Biology, 233 South 10th Street, BLSB 220 Philadelphia, Pennsylvania 19107, United States
| | - Haoyun Yang
- Department
of Chemistry and Biochemistry, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Joanna Sztuba-Solinska
- Vaccine
Research and Development, Early Bioprocess Development, Pfizer Inc., 401 N Middletown Road, Pearl
River, New York 10965, United States
| | - Shi-Jie Chen
- Department
of Physics and Astronomy, Department of Biochemistry, Institute of
Data Sciences and Informatics, University
of Missouri at Columbia, Columbia, Missouri 65211, United States
| | - Nikolay V. Dokholyan
- Departments
of Pharmacology and Biochemistry & Molecular Biology Penn State College of Medicine; Hershey, Pennsylvania 17033, United States
- Departments
of Chemistry and Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Cheemeng Tan
- University of California, Davis, California 95616, United States
| | - S. Kevin Li
- Division
of Pharmaceutical Sciences, James L Winkle
College of Pharmacy, University of Cincinnati, Cincinnati, Ohio 45267, United States
| | - Xiaoming He
- Fischell
Department of Bioengineering, University
of Maryland, College Park, Maryland 20742, United States
| | - Xiaoting Zhang
- Department
of Cancer Biology, Breast Cancer Research Program, and University
of Cincinnati Cancer Center, Vontz Center for Molecular Studies, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267, United States
| | - Wayne Miles
- Department
of Cancer Biology and Genetics, The Ohio
State University, Columbus, Ohio 43210, United States
| | - Elisa Franco
- Department
of Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, California 90024, United States
| | - Daniel W. Binzel
- Center
for RNA Nanobiotechnology and Nanomedicine; College of Pharmacy, James
Comprehensive Cancer Center, The Ohio State
University, Columbus, Ohio 43210, United States
| | - Peixuan Guo
- Center
for RNA Nanobiotechnology and Nanomedicine; College of Pharmacy, James
Comprehensive Cancer Center, The Ohio State
University, Columbus, Ohio 43210, United States
- Dorothy
M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, Ohio 43210, United States
| | - Kirill A. Afonin
- Nanoscale
Science Program, Department of Chemistry
University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
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3
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Kersten C, Archambault P, Köhler LP. Assessment of Nucleobase Protomeric and Tautomeric States in Nucleic Acid Structures for Interaction Analysis and Structure-Based Ligand Design. J Chem Inf Model 2024; 64:4485-4499. [PMID: 38766733 DOI: 10.1021/acs.jcim.4c00520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for pKa and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural C+G:C(A) and A+GG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.
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Affiliation(s)
- 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
| | - Philippe Archambault
- Chemical Computing Group, 910-1010 Sherbrooke W., Montreal, Quebec, Canada H3A 2R7
| | - Luca P Köhler
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
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4
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Guo M, Lin Y, Obi CD, Zhao P, Dailey HA, Medlock AE, Shen Y. Impact of Phosphorylation at Various Sites on the Active Pocket of Human Ferrochelatase: Insights from Molecular Dynamics Simulations. Int J Mol Sci 2024; 25:6360. [PMID: 38928065 PMCID: PMC11203519 DOI: 10.3390/ijms25126360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/27/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
Ferrochelatase (FECH) is the terminal enzyme in human heme biosynthesis, catalyzing the insertion of ferrous iron into protoporphyrin IX (PPIX) to form protoheme IX (Heme). Phosphorylation increases the activity of FECH, and it has been confirmed that the activity of FECH phosphorylated at T116 increases. However, it remains unclear whether the T116 site and other potential phosphorylation modification sites collaboratively regulate the activity of FECH. In this study, we identified a new phosphorylation site, T218, and explored the allosteric effects of unphosphorylated (UP), PT116, PT218, and PT116 + PT218 states on FECH in the presence and absence of substrates (PPIX and Heme) using molecular dynamics (MD) simulations. Binding free energies were evaluated with the MM/PBSA method. Our findings indicate that the PT116 + PT218 state exhibits the lowest binding free energy with PPIX, suggesting the strongest binding affinity. Additionally, this state showed a higher binding free energy with Heme compared to UP, which facilitates Heme release. Moreover, employing multiple analysis methods, including free energy landscape (FEL), principal component analysis (PCA), dynamic cross-correlation matrix (DCCM), and hydrogen bond interaction analysis, we demonstrated that phosphorylation significantly affects the dynamic behavior and binding patterns of substrates to FECH. Insights from this study provide valuable theoretical guidance for treating conditions related to disrupted heme metabolism, such as various porphyrias and iron-related disorders.
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Affiliation(s)
- Mingshan Guo
- School of Chemistry, IGCME, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuhong Lin
- School of Chemistry, IGCME, Sun Yat-sen University, Guangzhou 510006, China
| | - Chibuike David Obi
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA; (C.D.O.); (H.A.D.); (A.E.M.)
| | - Peng Zhao
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA;
| | - Harry A. Dailey
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA; (C.D.O.); (H.A.D.); (A.E.M.)
| | - Amy E. Medlock
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA; (C.D.O.); (H.A.D.); (A.E.M.)
- Augusta University/University of Georgia Medical Partnership, Athens, GA 30602, USA
| | - Yong Shen
- School of Chemistry, IGCME, Sun Yat-sen University, Guangzhou 510006, China
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5
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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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6
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Li J, Zhou Y, Chen SJ. Embracing exascale computing in nucleic acid simulations. Curr Opin Struct Biol 2024; 87:102847. [PMID: 38815519 DOI: 10.1016/j.sbi.2024.102847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/18/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024]
Abstract
This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Yuanzhe Zhou
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA.
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7
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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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8
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Xu L, Geng X, Li Q, Li M, Chen S, Liu X, Dai X, Zhu X, Wang X, Suo H. Calcium-based MOFs as scaffolds for shielding immobilized lipase and enhancing its stability. Colloids Surf B Biointerfaces 2024; 237:113836. [PMID: 38479261 DOI: 10.1016/j.colsurfb.2024.113836] [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: 12/17/2023] [Revised: 02/20/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024]
Abstract
The enzyme immobilization technology has become a key tool in the field of enzyme applications; however, improving the activity recovery and stability of the immobilized enzymes is still challenging. Herein, we employed a magnetic carboxymethyl cellulose (MCMC) nanocomposite modified with ionic liquids (ILs) for covalent immobilization of lipase, and used Ca-based metal-organic frameworks (MOFs) as the support skeleton and protective layer for immobilized enzymes. The ILs contained long side chains (eight CH2 units), which not only enhanced the hydrophobicity of the carrier and its hydrophobic interaction with the enzymes, but also provided a certain buffering effect when the enzyme molecules were subjected to compression. Compared to free lipase, the obtained CaBPDC@PPL-IL-MCMC exhibited higher specific activity and enhanced stability. In addition, the biocatalyst could be easily separated using a magnetic field, which is beneficial for its reusability. After 10 cycles, the residual activity of CaBPDC@PPL-IL-MCMC could reach up to 86.9%. These features highlight the good application prospects of the present immobilization method.
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Affiliation(s)
- Lili Xu
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Xinyue Geng
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Qi Li
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Moju Li
- School of Chemistry and Chemical Engineering, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Shu Chen
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Xiangnan Liu
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Xusheng Dai
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China
| | - Xiuhuan Zhu
- Liaocheng Customs of the People's Republic of China, China
| | - Xuekun Wang
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China.
| | - Hongbo Suo
- School of Pharmaceutical Sciences, Liaocheng University, Liaocheng, Shandong 252059, China.
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9
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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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10
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Tan LH, Kwoh CK, Mu Y. RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method. Brief Bioinform 2024; 25:bbae166. [PMID: 38695120 PMCID: PMC11063749 DOI: 10.1093/bib/bbae166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 05/04/2024] Open
Abstract
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.
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Affiliation(s)
- Lai Heng Tan
- Interdisciplinary Graduate School, Nanyang Technological University, 61 Nanyang Drive, 637335 Singapore, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551 Singapore, Singapore
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11
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Li J, Xu T, Zheng Y, Liu D, Zhang C, Li J, Wang ZA, Du Y. In Silico Study on a Binding Mechanism of ssDNA Aptamers Targeting Glycosidic Bond-Containing Small Molecules. Anal Chem 2024; 96:5056-5064. [PMID: 38497564 DOI: 10.1021/acs.analchem.4c00927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Aptamer-based detection targeting glycoconjugates has attracted significant attention for its remarkable potential in identifying structural changes in saccharides in different stages of various diseases. However, the challenges in screening aptamers for small carbohydrates or glycoconjugates, which contain highly flexible and diverse glycosidic bonds, have hindered their application and commercialization. In this study, we investigated the binding conformations between three glycosidic bond-containing small molecules (GlySMs; glucose, N-acetylneuraminic acid, and neomycin) and their corresponding aptamers in silico, and analyzed factors contributing to their binding affinities. Based on the findings, a novel binding mechanism was proposed, highlighting the central role of the stem structure of the aptamer in binding and recognizing GlySMs and the auxiliary role of the mismatched bases in the adjacent loop. Guided by this binding mechanism, an aptamer with a higher 6'-sialyllactose binding affinity was designed, achieving a KD value of 4.54 ± 0.64 μM in vitro through a single shear and one mutation. The binding mechanism offers crucial guidance for designing high-affinity aptamers, enhancing the virtual screening efficiency for GlySMs. This streamlined workflow filters out ineffective binding sites, accelerating aptamer development and providing novel insights into glycan-nucleic acid interactions.
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Affiliation(s)
- Jiaqing Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- School of Chemical Engineering, University of Chinese Academy of Sciences, No.19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Tong Xu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- School of Chemical Engineering, University of Chinese Academy of Sciences, No.19A Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Yalan Zheng
- Key Laboratory for Animal Disease-Resistant Nutrition of the Ministry of Education of China, Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan 611130, China
| | - Dongdong Liu
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
| | - Chen Zhang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
| | - Jianjun Li
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
| | - Zhuo A Wang
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
| | - Yuguang Du
- State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
- Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, 1 North second Street, Zhongguancun, Haidian District, Beijing 100190, China
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12
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Kaur J, Sharma A, Mundlia P, Sood V, Pandey A, Singh G, Barnwal RP. RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag. J Med Chem 2024. [PMID: 38498010 DOI: 10.1021/acs.jmedchem.3c01354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.
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Affiliation(s)
- Jaskirat Kaur
- Department of Biophysics, Panjab University, Chandigarh 160014, India
| | - Akanksha Sharma
- Department of Biophysics, Panjab University, Chandigarh 160014, India
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India
| | - Poonam Mundlia
- Department of Biophysics, Panjab University, Chandigarh 160014, India
| | - Vikas Sood
- Department of Biochemistry, Jamia Hamdard, New Delhi 110062, India
| | - Ankur Pandey
- Department of Chemistry, Panjab University, Chandigarh 160014, India
| | - Gurpal Singh
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India
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13
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Krishnan SR, Roy A, Gromiha MM. Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. Brief Bioinform 2024; 25:bbae002. [PMID: 38261341 PMCID: PMC10805179 DOI: 10.1093/bib/bbae002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 12/21/2023] [Accepted: 12/24/2023] [Indexed: 01/24/2024] Open
Abstract
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.
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Affiliation(s)
- Sowmya R Krishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - Arijit Roy
- TCS Research (Life Sciences division), Tata Consultancy Services, Hyderabad 500081, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- Department of Computer Science, National University of Singapore, Singapore 117543
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14
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Wang F, Xia R, Su Y, Cai P, Xu X. Quantifying RNA structures and interactions with a unified reduced chain representation model. Int J Biol Macromol 2023; 253:127181. [PMID: 37793523 DOI: 10.1016/j.ijbiomac.2023.127181] [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: 06/07/2023] [Revised: 08/30/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023]
Abstract
RNA is a pivotal molecule that plays critical roles in various cellular processes. Quantifying RNA structures and interactions is essential to understanding RNA function and developing RNA-based therapeutics. Using a unified five-bead model and a non-redundant database, this paper investigates the structural features and interactions of five commonly occurring RNA motifs, i.e., double-stranded helices, hairpin loops, internal/bulge loops, multi-branched junctions, and single-stranded terminal tails. Analyzing detailed distributions of RNA local structural features and base-base interactions reveals a preference for helical structures in both local backbone structures and base orientations. The interactions between adjacent bases exhibit motif-specific and sequence-dependent characteristics, reflecting the distinct topological constraints imposed by different loop-helix connection modes and the varying pairing and stacking interactions among different sequences. These findings shed light on the stability of RNA helices, emphasizing their significance in providing dominant base pairing and stacking interactions for RNA structures and stability. The four non-helix motifs encompass unpaired nucleotide loops and exhibit diverse base-base interactions, contributing to the structural diversity observed in RNA. Overall, the complexity of RNA structure arises from the intricate interplay of base-base interactions.
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Affiliation(s)
- Fengfei Wang
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Renjie Xia
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Yangyang Su
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China
| | - Pinggen Cai
- Department of Applied Physics, Zhejiang University of Technology, Hangzhou 310023, China.
| | - Xiaojun Xu
- Institute of Bioinformatics and Medical Engineering, School of Mathematics and Physics, Jiangsu University of Technology, Changzhou 213001, China.
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15
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Agarwal R, T RR, Smith JC. Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking. J Chem Inf Model 2023; 63:7444-7452. [PMID: 37972310 DOI: 10.1021/acs.jcim.3c01533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.
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Affiliation(s)
- Rupesh Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
| | - Rajitha Rajeshwar T
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6309, United States
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee 37996-1939, United States
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16
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Jacobsen L, Hungerland J, Bačić V, Gerhards L, Schuhmann F, Solov’yov IA. Introducing the Automated Ligand Searcher. J Chem Inf Model 2023; 63:7518-7528. [PMID: 37983165 PMCID: PMC10716895 DOI: 10.1021/acs.jcim.3c01317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 11/22/2023]
Abstract
The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.
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Affiliation(s)
- Luise Jacobsen
- Department
of Physics, Chemistry and Pharmacy, University
of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Jonathan Hungerland
- Institute
of Physics, Carl von Ossietzky Universität, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
| | - Vladimir Bačić
- Institute
of Physics, Carl von Ossietzky Universität, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
| | - Luca Gerhards
- Institute
of Physics, Carl von Ossietzky Universität, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
| | - Fabian Schuhmann
- Institute
of Physics, Carl von Ossietzky Universität, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
- Niels
Bohr International Academy, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
| | - Ilia A. Solov’yov
- Institute
of Physics, Carl von Ossietzky Universität, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
- Research
Centre for Neurosensory Science, Carl von
Ossietzky Universität Oldenburg, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
- Center
for Nanoscale Dynamics (CENAD), Carl von
Ossietzky Universität Oldenburg, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
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17
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Dodaro A, Pavan M, Menin S, Salmaso V, Sturlese M, Moro S. Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes. Front Mol Biosci 2023; 10:1294543. [PMID: 38028536 PMCID: PMC10679717 DOI: 10.3389/fmolb.2023.1294543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Ribonucleic acids are gradually becoming relevant players among putative drug targets, thanks to the increasing amount of structural data exploitable for the rational design of selective and potent binders that can modulate their activity. Mainly, this information allows employing different computational techniques for predicting how well would a ribonucleic-targeting agent fit within the active site of its target macromolecule. Due to some intrinsic peculiarities of complexes involving nucleic acids, such as structural plasticity, surface charge distribution, and solvent-mediated interactions, the application of routinely adopted methodologies like molecular docking is challenged by scoring inaccuracies, while more physically rigorous methods such as molecular dynamics require long simulation times which hamper their conformational sampling capabilities. In the present work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of unbinding kinetics, to characterize RNA-ligand complexes. In this article, we explored its applicability as a post-docking refinement tool on RNA in complex with small molecules, highlighting the capability of this method to identify the native binding mode among a set of decoys across various pharmaceutically relevant test cases.
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Affiliation(s)
| | | | | | | | | | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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18
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Karabaeva RZ, Vochshenkova TA, Zare A, Jafari N, Baneshi H, Mussin NM, Albayev RK, Kaliyev AA, Baspakova A, Tamadon A. Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses. Front Mol Biosci 2023; 10:1221337. [PMID: 37900914 PMCID: PMC10602687 DOI: 10.3389/fmolb.2023.1221337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction: Arterial hypertension (AH) is a pervasive global health concern with multifaceted origins encompassing both genetic and environmental components. Previous research has firmly established the association between AH and diverse genetic factors. Consequently, scientists have conducted extensive genetic investigations in recent years to unravel the intricate pathophysiology of AH. Methods: In this study, we conducted a comprehensive bibliometric analysis employing VOSviewer software to identify the most noteworthy genetic factors that have been the focal point of numerous investigations within the AH field in recent years. Our analysis revealed genes and microRNAs intricately linked to AH, underscoring their pivotal roles in this condition. Additionally, we performed molecular docking analyses to ascertain microRNAs with the highest binding affinity to these identified genes. Furthermore, we constructed a network to elucidate the in-silico-based functional interactions between the identified microRNAs and genes, shedding light on their potential roles in AH pathogenesis. Results: Notably, this pioneering in silico examination of genetic factors associated with AH promises novel insights into our understanding of this complex condition. Our findings prominently highlight miR-7110-5p, miR-7110-3p, miR-663, miR-328-3p, and miR-140-5p as microRNAs exhibiting a remarkable affinity for target genes. These microRNAs hold promise as valuable diagnostic and therapeutic factors, offering new avenues for the diagnosis and treatment of AH in the foreseeable future. Conclusion: In summary, this research underscores the critical importance of genetic factors in AH and, through in silico analyses, identifies specific microRNAs with significant potential for further investigation and clinical applications in AH management.
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Affiliation(s)
- Raushan Zh Karabaeva
- Gerontology Center, Medical Center of the President’s Affairs Administration of the Republic of Kazakhstan, Astana, Kazakhstan
- Therapeutic Department, Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | - Tamara A. Vochshenkova
- Gerontology Center, Medical Center of the President’s Affairs Administration of the Republic of Kazakhstan, Astana, Kazakhstan
- Therapeutic Department, Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
| | | | | | | | | | - Rustam Kuanyshbekovich Albayev
- Gerontology Center, Medical Center of the President’s Affairs Administration of the Republic of Kazakhstan, Astana, Kazakhstan
| | | | - Akmaral Baspakova
- Department for Scientific Work, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
| | - Amin Tamadon
- PerciaVista R&D Co., Shiraz, Iran
- Department for Scientific Work, West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan
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19
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Mathez G, Cagno V. Small Molecules Targeting Viral RNA. Int J Mol Sci 2023; 24:13500. [PMID: 37686306 PMCID: PMC10487773 DOI: 10.3390/ijms241713500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
The majority of antivirals available target viral proteins; however, RNA is emerging as a new and promising antiviral target due to the presence of highly structured RNA in viral genomes fundamental for their replication cycle. Here, we discuss methods for the identification of RNA-targeting compounds, starting from the determination of RNA structures either from purified RNA or in living cells, followed by in silico screening on RNA and phenotypic assays to evaluate viral inhibition. Moreover, we review the small molecules known to target the programmed ribosomal frameshifting element of SARS-CoV-2, the internal ribosomal entry site of different viruses, and RNA elements of HIV.
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Affiliation(s)
| | - Valeria Cagno
- Institute of Microbiology, University Hospital of Lausanne, University of Lausanne, 1011 Lausanne, Switzerland
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20
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Jiang D, Zhao H, Du H, Deng Y, Wu Z, Wang J, Zeng Y, Zhang H, Wang X, Wu J, Hsieh CY, Hou T. How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation. J Chem Theory Comput 2023; 19:5633-5647. [PMID: 37480347 DOI: 10.1021/acs.jctc.3c00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Nucleic acid (NA)-ligand interactions are of paramount importance in a variety of biological processes, including cellular reproduction and protein biosynthesis, and therefore, NAs have been broadly recognized as potential drug targets. Understanding NA-ligand interactions at the atomic scale is essential for investigating the molecular mechanism and further assisting in NA-targeted drug discovery. Molecular docking is one of the predominant computational approaches for predicting the interactions between NAs and small molecules. Despite the availability of versatile docking programs, their performance profiles for NA-ligand complexes have not been thoroughly characterized. In this study, we first compiled the largest structure-based NA-ligand binding data set to date, containing 800 noncovalent NA-ligand complexes with clearly identified ligands. Based on this extensive data set, eight frequently used docking programs, including six protein-ligand docking programs (LeDock, Surflex-Dock, UCSF Dock6, AutoDock, AutoDock Vina, and PLANTS) and two specific NA-ligand docking programs (rDock and RLDOCK), were systematically evaluated in terms of binding pose and binding affinity predictions. The results demonstrated that some protein-ligand docking programs, specifically PLANTS and LeDock, produced more promising or comparable results compared with the specialized NA-ligand docking programs. Among the programs evaluated, PLANTS, rDock, and LeDock showed the highest performance in binding pose prediction, and their top-1 and best root-mean-square deviation (rmsd) success rates were as follows: PLANTS (35.93 and 76.05%), rDock (27.25 and 72.16%), and LeDock (27.40 and 64.37%). Compared with the moderate level of binding pose prediction, few programs were successful in binding affinity prediction, and the best correlation (Rp = -0.461) was observed with PLANTS. Finally, further comparison with the latest NA-ligand docking program (NLDock) on four well-established data sets revealed that PLANTS and LeDock outperformed NLDock in terms of binding pose prediction on all data sets, demonstrating their significant potential for NA-ligand docking. To the best of our knowledge, this study is the most comprehensive evaluation of popular molecular docking programs for NA-ligand systems.
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Affiliation(s)
- Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Huifeng Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Hongyan Du
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yafeng Deng
- Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yundian Zeng
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Haotian Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Xiaorui Wang
- China State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau 999078, China
| | - Jian Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310006, Zhejiang, China
| | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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21
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Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents. Antibiotics (Basel) 2023; 12:antibiotics12030463. [PMID: 36978331 PMCID: PMC10044086 DOI: 10.3390/antibiotics12030463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Oxazolidinones are a broad-spectrum class of synthetic antibiotics that bind to the 50S ribosomal subunit of Gram-positive and Gram-negative bacteria. Many crystal structures of the ribosomes with oxazolidinone ligands have been reported in the literature, facilitating structure-based design using methods such as molecular docking. It would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. We examined the performance of five molecular docking programs (AutoDock 4, AutoDock Vina, DOCK 6, rDock, and RLDock) for their ability to model ribosomal–ligand interactions with oxazolidinones. Eleven ribosomal crystal structures with oxazolidinones as the ligands were docked. The accuracy was evaluated by calculating the docked complexes’ root-mean-square deviation (RMSD) and the program’s internal scoring function. The rankings for each program based on the median RMSD between the native and predicted were DOCK 6 > AD4 > Vina > RDOCK >> RLDOCK. Results demonstrate that the top-performing program, DOCK 6, could accurately replicate the ligand binding in only four of the eleven ribosomes due to the poor electron density of said ribosomal structures. In this study, we have further benchmarked the performance of the DOCK 6 docking algorithm and scoring in improving virtual screening (VS) enrichment using the dataset of 285 oxazolidinone derivatives against oxazolidinone binding sites in the S. aureus ribosome. However, there was no clear trend between the structure and activity of the oxazolidinones in VS. Overall, the docking performance indicates that the RNA pocket’s high flexibility does not allow for accurate docking prediction, highlighting the need to validate VS. protocols for ligand-RNA before future use. Later, we developed a re-scoring method incorporating absolute docking scores and molecular descriptors, and the results indicate that the descriptors greatly improve the correlation of docking scores and pMIC values. Morgan fingerprint analysis was also used, suggesting that DOCK 6 underpredicted molecules with tail modifications with acetamide, n-methylacetamide, or n-ethylacetamide and over-predicted molecule derivatives with methylamino bits. Alternatively, a ligand-based approach similar to a field template was taken, indicating that each derivative’s tail groups have strong positive and negative electrostatic potential contributing to microbial activity. These results indicate that one should perform VS. campaigns of ribosomal antibiotics with care and that more comprehensive strategies, including molecular dynamics simulations and relative free energy calculations, might be necessary in conjunction with VS. and docking.
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22
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Olenginski LT, Kasprzak WK, Attionu SK, Shapiro BA, Dayie TK. Virtual Screening of Hepatitis B Virus Pre-Genomic RNA as a Novel Therapeutic Target. Molecules 2023; 28:molecules28041803. [PMID: 36838792 PMCID: PMC9963113 DOI: 10.3390/molecules28041803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/07/2023] [Accepted: 02/11/2023] [Indexed: 02/17/2023] Open
Abstract
The global burden imposed by hepatitis B virus (HBV) infection necessitates the discovery and design of novel antiviral drugs to complement existing treatments. One attractive and underexploited therapeutic target is ε, an ~85-nucleotide (nt) cis-acting regulatory stem-loop RNA located at the 3'- and 5'-ends of the pre-genomic RNA (pgRNA). Binding of the 5'-end ε to the viral polymerase protein (P) triggers two early events in HBV replication: pgRNA and P packaging and reverse transcription. Our recent solution nuclear magnetic resonance spectroscopy structure of ε permits structure-informed drug discovery efforts that are currently lacking for P. Here, we employ a virtual screen against ε using a Food and Drug Administration (FDA)-approved compound library, followed by in vitro binding assays. This approach revealed that the anti-hepatitis C virus drug Daclatasvir is a selective ε-targeting ligand. Additional molecular dynamics simulations demonstrated that Daclatasvir targets ε at its flexible 6-nt priming loop (PL) bulge and modulates its dynamics. Given the functional importance of the PL, our work supports the notion that targeting ε dynamics may be an effective anti-HBV therapeutic strategy.
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Affiliation(s)
- Lukasz T. Olenginski
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
- Correspondence: author emails: (L.T.O.); (T.K.D.)
| | - Wojciech K. Kasprzak
- Bioinformatics and Computational Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Solomon K. Attionu
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Bruce A. Shapiro
- RNA Biology Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Theodore K. Dayie
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
- Correspondence: author emails: (L.T.O.); (T.K.D.)
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23
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Varghese N, Jose JR, Krishna PM, Philip D, Joy F, Vinod TP, Prathapachandra Kurup MR, Nair Y. In vitro
Analytical Techniques as Screening Tools to investigate the Metal chelate‐DNA interactions. ChemistrySelect 2023. [DOI: 10.1002/slct.202203615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Nikita Varghese
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
| | - Joyna Reba Jose
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
| | - P. Murali Krishna
- Department of Chemistry Ramaiah institute of technology MSRIT Post, M S Ramaiah Nagar Bengaluru 560054 Karnataka India
| | - Darit Philip
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
| | - Francis Joy
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
| | - T. P. Vinod
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
| | | | - Yamuna Nair
- Department of Chemistry CHRIST (Deemed to be University) Hosur Road Bengaluru 560 029 Karnataka India
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Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions. Comput Struct Biotechnol J 2022; 20:6120-6137. [PMID: 36420155 PMCID: PMC9672130 DOI: 10.1016/j.csbj.2022.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.
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Patel LA, Chau P, Debesai S, Darwin L, Neale C. Drug Discovery by Automated Adaptation of Chemical Structure and Identity. J Chem Theory Comput 2022; 18:5006-5024. [PMID: 35834740 DOI: 10.1021/acs.jctc.1c01271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.
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