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Antoniou M, Papavasileiou KD, Melagraki G, Dondero F, Lynch I, Afantitis A. Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists. Int J Mol Sci 2024; 25:5216. [PMID: 38791255 PMCID: PMC11121726 DOI: 10.3390/ijms25105216] [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: 04/01/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).
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Affiliation(s)
- Maria Antoniou
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
| | - Konstantinos D. Papavasileiou
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece;
| | - Francesco Dondero
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
- Department of Science and Technological Innovation, Università del Piemonte Orientale, 15121 Alessandria, Italy
| | - Iseult Lynch
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
- School of Geography, Earth and Environmental Sciences, University of Birmingham Edgbaston, Birmingham B15 2TT, UK
| | - Antreas Afantitis
- Department of Chemoinformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus; (M.A.); (K.D.P.)
- Department of ChemoInformatics, NovaMechanics MIKE, 18545 Piraeus, Greece
- Entelos Institute, Larnaca 6059, Cyprus; (F.D.); (I.L.)
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Chen P, Zhao N, Wang R, Chen G, Hu Y, Dou Z, Ban C. Hepatotoxicity and lipid metabolism disorders of 8:2 polyfluoroalkyl phosphate diester in zebrafish: In vivo and in silico evidence. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133807. [PMID: 38412642 DOI: 10.1016/j.jhazmat.2024.133807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/29/2024]
Abstract
8:2 polyfluoroalkyl phosphate diester (8:2 diPAP) has been shown to accumulate in the liver, but whether it induces hepatotoxicity and lipid metabolism disorders remains largely unknown. In this study, zebrafish embryos were exposed to 8:2 diPAP for 7 d. Hepatocellular hypertrophy and karyolysis were noted after exposure to 0.5 ng/L 8:2 diPAP, suggesting suppressed liver development. Compared to the water control, 8:2 diPAP led to significantly higher triglyceride and total cholesterol levels, but markedly lower levels of low-density lipoprotein, implying disturbed lipid homeostasis. The levels of two peroxisome proliferator activated receptor (PPAR) subtypes (pparα and pparγ) involved in hepatotoxicity and lipid metabolism were significantly upregulated by 8:2 diPAP, consistent with their overexpression as determined by immunohistochemistry. In silico results showed that 8:2 diPAP formed hydrogen bonds with PPARα and PPARγ. Among seven machine learning models, Adaptive Boosting performed the best in predicting the binding affinities of PPARα and PPARγ on the test set. The predicted binding affinity of 8:2 diPAP to PPARα (7.12) was higher than that to PPARγ (6.97) by Adaptive Boosting, which matched well with the experimental results. Our results revealed PPAR - mediated adverse effects of 8:2 diPAP on the liver and lipid metabolism of zebrafish larvae.
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Affiliation(s)
- Pengyu Chen
- Jiangsu Province Engineering Research Center for Marine Bio-resources Sustainable Utilization, College of Oceanography, Hohai University, Nanjing 210024, China; Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education, Hohai University, Nanjing 210024, China.
| | - Na Zhao
- Jiangsu Province Engineering Research Center for Marine Bio-resources Sustainable Utilization, College of Oceanography, Hohai University, Nanjing 210024, China
| | - Ruihan Wang
- Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Geng Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yuxi Hu
- Jiangsu Province Engineering Research Center for Marine Bio-resources Sustainable Utilization, College of Oceanography, Hohai University, Nanjing 210024, China
| | - Zhichao Dou
- Jiangsu Province Engineering Research Center for Marine Bio-resources Sustainable Utilization, College of Oceanography, Hohai University, Nanjing 210024, China
| | - Chenglong Ban
- Jiangsu Province Engineering Research Center for Marine Bio-resources Sustainable Utilization, College of Oceanography, Hohai University, Nanjing 210024, China
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Bolt MJ, Oceguera J, Singh PK, Safari K, Abbott DH, Neugebauer KA, Mancini MG, Gorelick DA, Stossi F, Mancini MA. Characterization of flavonoids with potent and subtype-selective actions on estrogen receptors alpha and beta. iScience 2024; 27:109275. [PMID: 38469564 PMCID: PMC10926205 DOI: 10.1016/j.isci.2024.109275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/05/2023] [Accepted: 02/15/2024] [Indexed: 03/13/2024] Open
Abstract
The initial step in estrogen-regulated transcription is the binding of a ligand to its cognate receptors, named estrogen receptors (ERα and ERβ). Phytochemicals present in foods and environment can compete with endogenous hormones to alter physiological responses. We screened 224 flavonoids in our engineered biosensor ERα and ERβ PRL-array cell lines to characterize their activity on several steps of the estrogen signaling pathway. We identified 83 and 96 flavonoids that can activate ERα or ERβ, respectively. While most act on both receptors, many appear to be subtype-selective, including potent flavonoids that activate ER at sub-micromolar concentrations. We employed an orthogonal assay using a transgenic zebrafish in vivo model that validated the estrogenic potential of these compounds. To our knowledge, this is the largest study thus far on flavonoids and the ER pathway, facilitating the identification of a new set of potential endocrine disruptors acting on both ERα and ERβ.
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Affiliation(s)
- Michael J. Bolt
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX 77030, USA
| | - Jessica Oceguera
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX 77030, USA
| | - Pankaj K. Singh
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX 77030, USA
| | - Kazem Safari
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX 77030, USA
| | - Derek H. Abbott
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kaley A. Neugebauer
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
- Center For Precision Environmental Health, Baylor College of Medicine, Houston, TX 77030, USA
| | - Maureen G. Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel A. Gorelick
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
- Center For Precision Environmental Health, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fabio Stossi
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Michael A. Mancini
- Center for Advanced Microscopy and Image Informatics, Institute of Biosciences & Technology, Texas A&M University, and Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX 77030, USA
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX 77030, USA
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Azhagiya Singam E, Durkin KA, La Merrill MA, Furlow JD, Wang JC, Smith MT. Prediction of the Interactions of a Large Number of Per- and Poly-Fluoroalkyl Substances with Ten Nuclear Receptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4487-4499. [PMID: 38422483 PMCID: PMC10938639 DOI: 10.1021/acs.est.3c05974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/02/2024]
Abstract
Per- and poly-fluoroalkyl substances (PFASs) are persistent, toxic chemicals that pose significant hazards to human health and the environment. Screening large numbers of chemicals for their ability to act as endocrine disruptors by modulating the activity of nuclear receptors (NRs) is challenging because of the time and cost of in vitro and in vivo experiments. For this reason, we need computational approaches to screen these chemicals and quickly prioritize them for further testing. Here, we utilized molecular modeling and machine-learning predictions to identify potential interactions between 4545 PFASs with ten different NRs. The results show that some PFASs can bind strongly to several receptors. Further, PFASs that bind to different receptors can have very different structures spread throughout the chemical space. Biological validation of these in silico findings should be a high priority.
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Affiliation(s)
| | - Kathleen A. Durkin
- Molecular
Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, California 94720, United States
| | - Michele A. La Merrill
- Department
of Environmental Toxicology, University
of California, Davis, California 95616, United States
| | - J. David Furlow
- Department
of Neurobiology, Physiology and Behavior, University of California, Davis California 95616, United States
| | - Jen-Chywan Wang
- Department
of Nutritional Sciences and Toxicology, University of California, Berkeley, California 94720, United States
| | - Martyn T. Smith
- Division
of Environmental Health Sciences, School of Public Health, University of California Berkeley, Berkeley, California 94720, United States
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Yang Z, Wang L, Yang Y, Pang X, Sun Y, Liang Y, Cao H. Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:2817-2829. [PMID: 38291630 DOI: 10.1021/acs.est.3c09779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.
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Affiliation(s)
- Zeguo Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ling Wang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Ying Yang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Xudi Pang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yuzhen Sun
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Yong Liang
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Huiming Cao
- Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China
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Wang Q, Wang Z, Tian S, Wang L, Tang R, Yu Y, Ge J, Hou T, Hao H, Sun H. Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning. J Chem Inf Model 2022; 62:3993-4007. [PMID: 36040137 DOI: 10.1021/acs.jcim.2c00851] [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
The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.
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Affiliation(s)
- Qinghua Wang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Sheng Tian
- Department of Medicinal Chemistry, College of Pharmaceutical Sciences, Soochow University, Suzhou 215123, P. R. China
| | - Lingling Wang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Rongfan Tang
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Yang Yu
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
| | - Jingxuan Ge
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, Key Lab of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, 210009 Nanjing, China
| | - Huiyong Sun
- Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China
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Wang J, Lou C, Liu G, Li W, Wu Z, Tang Y. Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening. Brief Bioinform 2022; 23:6673852. [PMID: 35998896 DOI: 10.1093/bib/bbac351] [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: 04/12/2022] [Revised: 07/13/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.
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Affiliation(s)
- Jiye Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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