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Schaduangrat N, Anuwongcharoen N, Charoenkwan P, Shoombuatong W. DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. J Cheminform 2023; 15:50. [PMID: 37149650 PMCID: PMC10163717 DOI: 10.1186/s13321-023-00721-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/08/2023] [Indexed: 05/08/2023] Open
Abstract
Drug resistance represents a major obstacle to therapeutic innovations and is a prevalent feature in prostate cancer (PCa). Androgen receptors (ARs) are the hallmark therapeutic target for prostate cancer modulation and AR antagonists have achieved great success. However, rapid emergence of resistance contributing to PCa progression is the ultimate burden of their long-term usage. Hence, the discovery and development of AR antagonists with capability to combat the resistance, remains an avenue for further exploration. Therefore, this study proposes a novel deep learning (DL)-based hybrid framework, named DeepAR, to accurately and rapidly identify AR antagonists by using only the SMILES notation. Specifically, DeepAR is capable of extracting and learning the key information embedded in AR antagonists. Firstly, we established a benchmark dataset by collecting active and inactive compounds against AR from the ChEMBL database. Based on this dataset, we developed and optimized a collection of baseline models by using a comprehensive set of well-known molecular descriptors and machine learning algorithms. Then, these baseline models were utilized for creating probabilistic features. Finally, these probabilistic features were combined and used for the construction of a meta-model based on a one-dimensional convolutional neural network. Experimental results indicated that DeepAR is a more accurate and stable approach for identifying AR antagonists in terms of the independent test dataset, by achieving an accuracy of 0.911 and MCC of 0.823. In addition, our proposed framework is able to provide feature importance information by leveraging a popular computational approach, named SHapley Additive exPlanations (SHAP). In the meanwhile, the characterization and analysis of potential AR antagonist candidates were achieved through the SHAP waterfall plot and molecular docking. The analysis inferred that N-heterocyclic moieties, halogenated substituents, and a cyano functional group were significant determinants of potential AR antagonists. Lastly, we implemented an online web server by using DeepAR (at http://pmlabstack.pythonanywhere.com/DeepAR ). We anticipate that DeepAR could be a useful computational tool for community-wide facilitation of AR candidates from a large number of uncharacterized compounds.
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Affiliation(s)
- Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nuttapat Anuwongcharoen
- Department of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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2
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Goyal S, Rani P, Chahar M, Hussain K, Kumar P, Sindhu J. Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies. J Biomol Struct Dyn 2023; 41:13616-13631. [PMID: 37010991 DOI: 10.1080/07391102.2023.2193991] [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: 10/11/2022] [Accepted: 02/03/2023] [Indexed: 04/04/2023]
Abstract
Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R2validation = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. In silico molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of -10.46 to -14.80. Molecular dynamics simulation of 100 ns was performed for ED01 and NED05. The results revealed that the protein-ligand complex bearing NED05 was more stable than lead ED01 exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Surbhi Goyal
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Payal Rani
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Monika Chahar
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Khalid Hussain
- Department of AS&H, Mewat Engineering College, Palla, Nuh, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
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Banerjee A, De P, Kumar V, Kar S, Roy K. Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across. CHEMOSPHERE 2022; 309:136579. [PMID: 36174732 DOI: 10.1016/j.chemosphere.2022.136579] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 09/18/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Priyanka De
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, United States
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability. Mol Divers 2022; 26:2847-2862. [PMID: 35767129 DOI: 10.1007/s11030-022-10478-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 12/21/2022]
Abstract
Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.
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Studies on the antibacterial activities and molecular mechanism of GyrB inhibitors by 3D-QSAR, molecular docking and molecular dynamics simulation. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
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Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
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7
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Wang F, Yang W, Li R, Sui Z, Cheng G, Zhou B. Molecular description of pyrimidine-based inhibitors with activity against FAK combining 3D-QSAR analysis, molecular docking and molecular dynamics. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103144] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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8
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Investigation of Quantitative Structure Activity Relationship of Isatin-based Oxadiazole Derivatives as Thymidine Phosphorylase Inhibitors. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2021. [DOI: 10.1016/s1872-2040(21)60095-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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9
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Emara Y, Fantke P, Judson R, Chang X, Pradeep P, Lehmann A, Siegert MW, Finkbeiner M. Integrating endocrine-related health effects into comparative human toxicity characterization. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 762:143874. [PMID: 33401053 DOI: 10.1016/j.scitotenv.2020.143874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 06/12/2023]
Abstract
Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.
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Affiliation(s)
- Yasmine Emara
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
| | - Richard Judson
- Office of Research and Development, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, LLC., Morrisville, NC 27560, United States.
| | - Prachi Pradeep
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Annekatrin Lehmann
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Marc-William Siegert
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Matthias Finkbeiner
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
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Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020; 25:E5277. [PMID: 33198233 PMCID: PMC7696134 DOI: 10.3390/molecules25225277] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 12/30/2022] Open
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
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Affiliation(s)
- Lauv Patel
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Tripti Shukla
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, AR 72467, USA;
| | - David W. Ussery
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Shanzhi Wang
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
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Shao WB, Zheng YT, Liu JM, Fu YH, Qi PY, Zhou X, Wu ZB, Wang PY, Yang S. Antibacterial activities against Ralstonia solanacearum and Xanthomonas oryzae pv. oryzae of 6-chloro-4-(4-substituted piperazinyl)quinazoline derivatives. Bioorg Med Chem Lett 2020; 30:126912. [DOI: 10.1016/j.bmcl.2019.126912] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 12/13/2019] [Accepted: 12/15/2019] [Indexed: 02/08/2023]
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12
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Sakkiah S, Guo W, Pan B, Kusko R, Tong W, Hong H. Computational prediction models for assessing endocrine disrupting potential of chemicals. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2019; 36:192-218. [PMID: 30633647 DOI: 10.1080/10590501.2018.1537132] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.
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Affiliation(s)
- Sugunadevi Sakkiah
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Wenjing Guo
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Bohu Pan
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Rebecca Kusko
- b Immuneering Corporation , Cambridge , Massachusetts , USA
| | - Weida Tong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Huixiao Hong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
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Sakkiah S, Kusko R, Pan B, Guo W, Ge W, Tong W, Hong H. Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations. Front Pharmacol 2018; 9:492. [PMID: 29867496 PMCID: PMC5962723 DOI: 10.3389/fphar.2018.00492] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 04/25/2018] [Indexed: 01/28/2023] Open
Abstract
When a small molecule binds to the androgen receptor (AR), a conformational change can occur which impacts subsequent binding of co-regulator proteins and DNA. In order to accurately study this mechanism, the scientific community needs a crystal structure of the Wild type AR (WT-AR) ligand binding domain, bound with antagonist. To address this open need, we leveraged molecular docking and molecular dynamics (MD) simulations to construct a structure of the WT-AR ligand binding domain bound with antagonist bicalutamide. The structure of mutant AR (Mut-AR) bound with this same antagonist informed this study. After molecular docking analysis pinpointed the suitable binding orientation of a ligand in AR, the model was further optimized through 1 μs of MD simulations. Using this approach, three molecular systems were studied: (1) WT-AR bound with agonist R1881, (2) WT-AR bound with antagonist bicalutamide, and (3) Mut-AR bound with bicalutamide. Our structures were very similar to the experimentally determined structures of both WT-AR with R1881 and Mut-AR with bicalutamide, demonstrating the trustworthiness of this approach. In our model, when WT-AR is bound with bicalutamide, Val716/Lys720/Gln733, or Met734/Gln738/Glu897 move and thus disturb the positive and negative charge clumps of the AF2 site. This disruption of the AF2 site is key for understanding the impact of antagonist binding on subsequent co-regulator binding. In conclusion, the antagonist induced structural changes in WT-AR detailed in this study will enable further AR research and will facilitate AR targeting drug discovery.
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Affiliation(s)
- Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Rebecca Kusko
- Immuneering Corporation, Cambridge, MA, United States
| | - Bohu Pan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Wenjing Guo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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14
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Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. Oncotarget 2018; 9:16899-16916. [PMID: 29682193 PMCID: PMC5908294 DOI: 10.18632/oncotarget.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 02/01/2018] [Indexed: 12/21/2022] Open
Abstract
The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.
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Sakkiah S, Wang T, Zou W, Wang Y, Pan B, Tong W, Hong H. Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 15:ijerph15010025. [PMID: 29295509 PMCID: PMC5800125 DOI: 10.3390/ijerph15010025] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 12/13/2017] [Accepted: 12/20/2017] [Indexed: 02/06/2023]
Abstract
Endocrine disrupting chemicals (EDCs) can mimic natural hormone to interact with receptors in the endocrine system and thus disrupt the functions of the endocrine system, raising concerns on the public health. In addition to disruption of the endocrine system, some EDCs have been found associated with many diseases such as breast cancer, prostate cancer, infertility, asthma, stroke, Alzheimer’s disease, obesity, and diabetes mellitus. EDCs that binding androgen receptor have been reported associated with diabetes mellitus in in vitro, animal, and clinical studies. In this review, we summarize the structural basis and interactions between androgen receptor and EDCs as well as the associations of various types of diabetes mellitus with the EDCs mediated through androgen receptor binding. We also discuss the perspective research for further understanding the impact and mechanisms of EDCs on the risk of diabetes mellitus.
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Affiliation(s)
- Sugunadevi Sakkiah
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Tony Wang
- Department of Biology, Arkansas University, Fayetteville, AR 72701, USA.
| | - Wen Zou
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Yuping Wang
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Bohu Pan
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Huixiao Hong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
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Selvaraj C, Sakkiah S, Tong W, Hong H. Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food Chem Toxicol 2017; 112:495-506. [PMID: 28843597 DOI: 10.1016/j.fct.2017.08.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/08/2017] [Accepted: 08/22/2017] [Indexed: 12/13/2022]
Abstract
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.
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Affiliation(s)
- Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides. Sci Rep 2016; 6:32115. [PMID: 27558848 PMCID: PMC4997263 DOI: 10.1038/srep32115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 08/02/2016] [Indexed: 12/19/2022] Open
Abstract
Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
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Liu H, Yang X, Lu R. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin. CHEMOSPHERE 2016; 156:1-7. [PMID: 27156209 DOI: 10.1016/j.chemosphere.2016.04.077] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 04/13/2016] [Accepted: 04/20/2016] [Indexed: 06/05/2023]
Abstract
Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) to disrupt endocrine function. However, this mechanism has not gotten enough attention, compared with that of hormone receptors and synthetase up to now, especially for the sex hormone transport process. In this study, we selected sex hormone-binding globulin (SHBG) and EDCs as a model system and the relative competing potency of a chemical with testosterone binding to SHBG (log RBA) as the endpoints, to develop classification models and quantitative structure-activity relationship (QSAR) models. With the classification model, a satisfactory model with nR09, nR10 and RDF155v as the most relevant variables was screened. Statistic results indicated that the model had the sensitivity, specificity, accuracy of 86.4%, 80.0%, 84.4% and 85.7%, 87.5%, 86.2% for the training set and validation set, respectively, highlighting a high classification performance of the model. With the QSAR model, a satisfactory model with statistical parameters, specifically, an adjusted determination coefficient (Radj(2)) of 0.810, a root mean square error (RMSE) of 0.616, a leave-one-out cross-validation squared correlation coefficient (QLOO(2)) of 0.777, a bootstrap method (QBOOT(2)) of 0.756, an external validation coefficient (Qext(2)) of 0.544 and a RMSEext of 0.859, were obtained, which implied satisfactory goodness of fit, robustness and predictive ability. The applicability domain of the current model covers a large number of structurally diverse chemicals, especially a few classes of nonsteroidal compounds.
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Affiliation(s)
- Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China.
| | - Xianhai Yang
- Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Jiang-wang-miao Street, Nanjing, 210042, China
| | - Rui Lu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China
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Sakkiah S, Ng HW, Tong W, Hong H. Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery. Expert Opin Ther Targets 2016; 20:1267-82. [PMID: 27195510 DOI: 10.1080/14728222.2016.1192131] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Androgen receptor (AR) is a ligand-dependent transcription factor and a member of the nuclear receptor superfamily. It plays a vital role in male sexual development and regulates gene expression in various tissues, including prostate. Androgens are compounds that exert their biological effects via interaction with AR. Binding of androgens to AR initiates conformational changes in AR that affect binding of co-regulator proteins and DNA. AR agonists and antagonists are widely used in a variety of clinical applications (i.e. hypogonadism and prostate cancer therapy). AREAS COVERED This review provides a close look at structures of AR-ligand complexes and mutations in the receptor that have been revealed, discusses current challenges in the field, and sheds light on future directions. EXPERT OPINION AR is one of the primary targets for the treatment of prostate cancer, as AR antagonists inhibit prostate cancer growth. However, these drugs are not effective for long-term treatment and lead to castration-resistant prostate cancer. The structures of AR-ligand complexes are an invaluable scientific asset that enhances our understanding of biological functions and mechanisms of androgenic and anti-androgenic chemicals as well as promotes the discovery of superior drug candidates.
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Affiliation(s)
- Sugunadevi Sakkiah
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Hui Wen Ng
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Weida Tong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Huixiao Hong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
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Wilkes JG, Stoyanova-Slavova IB, Buzatu DA. Alignment-independent technique for 3D QSAR analysis. J Comput Aided Mol Des 2016; 30:331-45. [PMID: 27026022 PMCID: PMC4833814 DOI: 10.1007/s10822-016-9909-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 03/08/2016] [Indexed: 11/25/2022]
Abstract
Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.
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Affiliation(s)
- Jon G. Wilkes
- Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Iva B. Stoyanova-Slavova
- Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079 USA
| | - Dan A. Buzatu
- Division of Systems Biology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079 USA
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21
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Norinder U, Rybacka A, Andersson PL. Conformal prediction to define applicability domain - A case study on predicting ER and AR binding. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:303-316. [PMID: 27088868 DOI: 10.1080/1062936x.2016.1172665] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
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Affiliation(s)
- U Norinder
- a Swedish Toxicology Sciences Research Center , Södertälje , Sweden
- b Department of Computer and Systems Sciences , Stockholm University , Kista , Sweden
| | - A Rybacka
- c Department of Chemistry , Umeå University , Umeå , Sweden
| | - P L Andersson
- c Department of Chemistry , Umeå University , Umeå , Sweden
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22
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Hong H, Shen J, Ng HW, Sakkiah S, Ye H, Ge W, Gong P, Xiao W, Tong W. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:372. [PMID: 27023588 PMCID: PMC4847034 DOI: 10.3390/ijerph13040372] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 03/10/2016] [Accepted: 03/22/2016] [Indexed: 11/21/2022]
Abstract
Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.
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Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Jie Shen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA.
| | - Wenming Xiao
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
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Abstract
Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.
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Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
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24
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Ng HW, Doughty SW, Luo H, Ye H, Ge W, Tong W, Hong H. Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets. Chem Res Toxicol 2015; 28:2343-51. [PMID: 26524122 DOI: 10.1021/acs.chemrestox.5b00358] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.
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Affiliation(s)
- Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Stephen W Doughty
- School of Pharmacy, University of Nottingham Malaysia Campus , Jalan Broga, 43500 Semenyih, Selangor, Malaysia
| | - Heng Luo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
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Molecular determinants of thyroid hormone receptor selectivity in a series of phosphonic acid derivatives: 3D-QSAR analysis and molecular docking. Chem Biol Interact 2015; 240:324-35. [DOI: 10.1016/j.cbi.2015.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 07/16/2015] [Accepted: 09/03/2015] [Indexed: 11/20/2022]
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Ng HW, Shu M, Luo H, Ye H, Ge W, Perkins R, Tong W, Hong H. Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds. Chem Res Toxicol 2015; 28:1784-95. [PMID: 26308263 DOI: 10.1021/acs.chemrestox.5b00243] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.
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Affiliation(s)
- Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Mao Shu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Heng Luo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Roger Perkins
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
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Babu S, Sohn H, Madhavan T. Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA and CoMSIA approach. Comput Biol Chem 2015; 56:109-21. [PMID: 25935115 DOI: 10.1016/j.compbiolchem.2015.04.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 04/07/2015] [Accepted: 04/18/2015] [Indexed: 10/23/2022]
Abstract
CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q(2)>0.4, r(2)>0.5 and r(2)pred>0.5. Based on better q(2) and r(2)pred values, the best predictions were obtained for the CoMFA (model 5 q(2)=0.488, r(2)pred=0.732), and CoMSIA (model 45 q(2)=0.525, r(2)pred=0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.
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Affiliation(s)
- Sathya Babu
- Department of Bioinformatics, School of Bioengineering, SRM University, SRM Nagar, Kattankulathur, Chennai 603203, India
| | - Honglae Sohn
- Department of Chemistry and Department of Carbon Materials, Chosun University, Gwangju 501-759, South Korea.
| | - Thirumurthy Madhavan
- Department of Bioinformatics, School of Bioengineering, SRM University, SRM Nagar, Kattankulathur, Chennai 603203, India.
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Ng HW, Zhang W, Shu M, Luo H, Ge W, Perkins R, Tong W, Hong H. Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists. BMC Bioinformatics 2014; 15 Suppl 11:S4. [PMID: 25349983 PMCID: PMC4251048 DOI: 10.1186/1471-2105-15-s11-s4] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders. Methods The approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands. Results The performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis. Conclusion This approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.
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29
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Ginex T, Dall’Asta C, Cozzini P. Preliminary Hazard Evaluation of Androgen Receptor-Mediated Endocrine-Disrupting Effects of Thioxanthone Metabolites through Structure-Based Molecular Docking. Chem Res Toxicol 2014; 27:279-89. [DOI: 10.1021/tx400383p] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Tiziana Ginex
- Molecular
Modelling Laboratory, Department of Food Science, University of Parma, Parco Area delle Scienze, 17/A, 43124 Parma, Italy
| | - Chiara Dall’Asta
- Department
of Food Science, University of Parma, Parco Area delle Scienze, 59/A, 43124 Parma, Italy
| | - Pietro Cozzini
- Molecular
Modelling Laboratory, Department of Food Science, University of Parma, Parco Area delle Scienze, 17/A, 43124 Parma, Italy
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Li X, Ye L, Shi W, Liu H, Liu C, Qian X, Zhu Y, Yu H. In silico study on hydroxylated polychlorinated biphenyls as androgen receptor antagonists. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2013; 92:258-264. [PMID: 23582771 DOI: 10.1016/j.ecoenv.2013.03.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2012] [Revised: 03/04/2013] [Accepted: 03/05/2013] [Indexed: 06/02/2023]
Abstract
Hydroxylated polychlorinated biphenyls (HO-PCBs), major metabolites of PCBs, may have the potential to disrupt androgen hormone homeostasis. However, there is a lack of systematic investigation into the intermolecular interaction mechanism between HO-PCBs and the androgen receptor (AR). In this study, the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations was performed to elucidate structural characteristics that influence the anti-androgen activity of HO-PCBs, and to provide a better understanding of the binding modes between HO-PCBs and AR. A predictive comparative molecular field analysis (CoMFA) model was developed with good robustness and predictive ability. Graphical interpretation of the model provided some insights into the structural features that affect the anti-androgen activity of HO-PCBs. The hydrogen bond interaction with Gln711, and hydrophobic interactions with residues in the hydrophobic pocket played important roles in the binding of ligand with receptor. These results are expected to be beneficial to predict anti-androgen activities of other HO-PCBs and provided possible clues for further elucidation of the binding mechanism of HO-PCBs with AR.
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Affiliation(s)
- Xiaolin Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
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Lack of androgenicity and estrogenicity of the three monomers used in Eastman’s Tritan™ copolyesters. Food Chem Toxicol 2012; 50:2196-205. [DOI: 10.1016/j.fct.2012.02.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2011] [Revised: 01/25/2012] [Accepted: 02/08/2012] [Indexed: 01/06/2023]
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Liu Z, Kelly R, Fang H, Ding D, Tong W. Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships. Chem Res Toxicol 2011; 24:1062-70. [PMID: 21627106 DOI: 10.1021/tx2000637] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the TGx models' predictive accuracy (0.77, 0.77, and 0.82 for the 1-day, 3-day, and 5-day models, respectively) was much higher for an independent validation set than that of a QSAR model (0.55). Permutation tests confirmed the statistical significance of the model's prediction performance. The study concluded that a short-term 5-day TGx animal model holds the potential to predict nongenotoxic hepatocarcinogenicity.
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Affiliation(s)
- Zhichao Liu
- Center of Excellence for Bioinformatics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA
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Todorov M, Mombelli E, Ait-Aissa S, Mekenyan O. Androgen receptor binding affinity: a QSAR evaluation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2011; 22:265-291. [PMID: 21598194 DOI: 10.1080/1062936x.2011.569508] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
The multiparameter formulation of the COmmon REactivity PAttern (COREPA) approach has been used to describe the structural requirements for eliciting rat androgen receptor (AR) binding affinity, accounting for molecular flexibility. Chemical affinity for AR binding was related to the distances between nucleophilic sites and structural features describing electronic and hydrophobic interactions between the receptor and ligands. Categorical models were derived for each binding affinity range in terms of specific distances, local (maximal donor delocalizability associated with the oxygen atom of the A ring), global nucleophilicity (partial positive surface areas and energy of the highest occupied molecular orbital) and hydrophobicity (log Kow) of the molecules. An integral screening tool for predicting binding affinity to AR was constructed as a battery of models, each associated with different activity bins. The quality of the screening battery of models was assessed using a high value (0.9) of the Pearson contingency coefficient. The predictability of the model was assessed by testing the model performance on external validation sets. A recently developed technique for selection of potential androgenically active chemicals was used to test the performance of the model in its applicability domain. Some of the selected chemicals were tested for AR transcriptional activation. The experimental results confirmed the theoretical predictions.
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Affiliation(s)
- M Todorov
- Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bourgas, Bulgaria
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Madden JC, Cronin MTD. Three-Dimensional Molecular Modelling of Receptor-Based Mechanisms in Toxicology. IN SILICO TOXICOLOGY 2010. [DOI: 10.1039/9781849732093-00210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Previous chapters have discussed the generation and use of relatively simple descriptors (such as log P, topological descriptors etc) in predicting toxicity; such descriptors alone can accurately predict certain endpoints. However, other endpoints require a more complex modelling process. Molecules exist as 3-dimensional entities and where toxicity is the result of specific spatially-related interactions between the toxicant and a biological macromolecule, for example receptor-mediated effects, models must be able to take into account this 3-dimensional interaction. This chapter will present a brief overview of the use of ligand-based and receptors-based 3-dimensional approaches in toxicity prediction. An introduction to relevant software and example case studies where the approaches have been successfully employed will be presented.
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Affiliation(s)
- J. C. Madden
- School of Pharmacy and Chemistry, Liverpool John Moores University Byrom Street Liverpool L3 3AF UK
| | - M. T. D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University Byrom Street Liverpool L3 3AF UK
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Abstract
BACKGROUND Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990's by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing. RESULTS The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. CONCLUSION The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at http://edkb.fda.gov/webstart/edkb/index.html.
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Kortagere S, Ekins S. Troubleshooting computational methods in drug discovery. J Pharmacol Toxicol Methods 2010; 61:67-75. [PMID: 20176118 DOI: 10.1016/j.vascn.2010.02.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2010] [Accepted: 02/11/2010] [Indexed: 10/19/2022]
Abstract
Computational approaches for drug discovery such as ligand-based and structure-based methods, are increasingly seen as an efficient approach for lead discovery as well as providing insights on absorption, distribution, metabolism, excretion and toxicity (ADME/Tox). What is perhaps less well known and widely described are the limitations of the different technologies. We have therefore presented a troubleshooting approach to QSAR, homology modeling, docking as well as hybrid methods. If such computational or cheminformatics methods are to become more widely used by non-experts it is critical that such limitations are brought to the user's attention and addressed during their workflows. This could improve the quality of the models and results that are obtained.
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Affiliation(s)
- Sandhya Kortagere
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA 19129, USA.
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Filimonov DA, Zakharov AV, Lagunin AA, Poroikov VV. QNA-based 'Star Track' QSAR approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2009; 20:679-709. [PMID: 20024804 DOI: 10.1080/10629360903438370] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
In the existing quantitative structure-activity relationship (QSAR) methods any molecule is represented as a single point in a many-dimensional space of molecular descriptors. We propose a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In the 'Star Track' methodology any molecule is represented as a set of points in a two-dimensional space of QNA descriptors. With our new method the estimate of the target property of a chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptor space. Substantially, we propose the use of only two descriptors rather than more than 3000 molecular descriptors that apply in the QSAR method. On the basis of this approach we have developed the computer program GUSAR and compared it with several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. We show that in the majority of cases the accuracy and predictivity of GUSAR models appears to be better than those for the reference QSAR methods. High predictive ability and robustness of GUSAR are also shown in the leave-20%-out cross-validation procedure.
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Affiliation(s)
- D A Filimonov
- Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, Moscow, Russia.
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Li Y, Wang Y, Ding J, Wang Y, Chang Y, Zhang S. In silico Prediction of Androgenic and Nonandrogenic Compounds Using Random Forest. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200810100] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Panaye A, Doucet JP, Devillers J, Marchand-Geneste N, Porcher JM. Decision trees versus support vector machine for classification of androgen receptor ligands. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2008; 19:129-151. [PMID: 18311640 DOI: 10.1080/10629360701843441] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.
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Affiliation(s)
- A Panaye
- ITODYS, Université Paris 7 Denis Diderot, UMR7086 CNRS, Paris, France.
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Cronin M, Worth A. (Q)SARs for Predicting Effects Relating to Reproductive Toxicity. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200710118] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Devillers J, Marchand-Geneste N, Carpy A, Porcher JM. SAR and QSAR modeling of endocrine disruptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:393-412. [PMID: 16920661 DOI: 10.1080/10629360600884397] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.
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Affiliation(s)
- J Devillers
- CTIS, 3 Chemin de la Gravière, 69140 Rillieux La Pape, France.
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Tamura H, Ishimoto Y, Fujikawa T, Aoyama H, Yoshikawa H, Akamatsu M. Structural basis for androgen receptor agonists and antagonists: interaction of SPEED 98-listed chemicals and related compounds with the androgen receptor based on an in vitro reporter gene assay and 3D-QSAR. Bioorg Med Chem 2006; 14:7160-74. [PMID: 16876421 DOI: 10.1016/j.bmc.2006.06.064] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2006] [Revised: 06/24/2006] [Accepted: 06/28/2006] [Indexed: 10/24/2022]
Abstract
The androgen receptor (AR) activity of listed chemicals, so called SPEED 98, by the Ministry of the Environment, Japan, and structurally related chemicals was characterized using MDA-kb2 human breast cancer cells stably expressing an androgen-responsive luciferase reporter gene, MMTV-luc. Since our results suggested that chemicals with diverse chemical structures were capable of disrupting the endocrine systems mediated by AR, a comparative molecular field analysis (CoMFA) model was developed to analyze the structural requirements necessary to disrupt AR function. A significant CoMFA model with r(2)=0.825 and q(2)=0.332 was developed for AR antagonist activity of 35 pure antagonists excluding procymidone. On the other hand, a good CoMFA model with r(2)=0.983 and q(2)=0.555 was obtained for antagonist activity of 13 chemicals with both agonist and antagonist activities. The steric and electrostatic properties were sufficient to describe the structural requirements for AR antagonist activity. In addition, the structural difference of AR agonists and antagonists was explained based on CoMFA results and the AR-LBD crystal structure. As several ERalpha agonists such as diethylstilbestrol (DES) acted as AR antagonists, the surface area of the AR ligand-binding domain (LBD) was compared with that of the ERalpha-LBD based on their reported crystal structures to analyze how those ligands interact with LBDs. The surface area of AR-LBD was shown to be smaller than that of ERalpha-LBD and therefore compounds with both estrogenic and antiandrogenic activities can fit well into the ERalpha-LBD but may protrude from the AR-LBD. It is likely that this subtle difference of the surface areas of the LBDs determines whether an ERalpha agonist acts as an AR antagonist or an agonist.
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Affiliation(s)
- Hiroto Tamura
- Department of Environmental Bioscience, Meijo University, Nagoya 468-8502, Japan
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Zhao CY, Zhang RS, Zhang HX, Xue CX, Liu HX, Liu MC, Hu ZD, Fan BT. QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2005; 16:349-67. [PMID: 16234176 DOI: 10.1080/10659360500204368] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.
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Affiliation(s)
- C Y Zhao
- Lanzhou University, Department of Chemistry, Lanzhou 730000, China. zhaocy@
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Nordin H, Jungnelius M, Karlsson R, Karlsson OP. Kinetic studies of small molecule interactions with protein kinases using biosensor technology. Anal Biochem 2005; 340:359-68. [PMID: 15840510 DOI: 10.1016/j.ab.2005.02.027] [Citation(s) in RCA: 79] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2004] [Indexed: 11/26/2022]
Abstract
Protein kinases are among the most commonly targeted groups of molecules in drug discovery today. Despite this, there are few examples of using surface plasmon resonance (SPR) for kinase inhibitor interaction studies, probably reflecting the need for better developed assays for these proteins. In this article, we present a general methodology that uses biosensor technology to study small molecule binding to eight different serine/threonine and tyrosine kinases. Mild immobilization conditions and a carefully composed assay buffer were identified as key success factors. The methodology package consists of direct binding studies of compounds to immobilized kinases, kinase activity assays to confirm inhibitory effects, detailed kinetic analyses of inhibitor binding, and competition assays with ATP for identification of competitive inhibitors. The kinetic assays resolve affinity into the rates of inhibitor binding and dissociation. Therefore, more detailed information on the relation between inhibitor structure and function is obtained. This might be of key importance for the development of effective kinase inhibitors.
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Affiliation(s)
- Helena Nordin
- Department of Biochemistry and Chemistry, Biacore AB, Rapsgatan 7, SE-754 50 Uppsala, Sweden
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