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Title: Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents

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 themore » 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.« less

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research. Office of Research. Division of Bioinformatics and Biostatistics
  2. U.S. Food and Drug Administration (FDA), Silver Spring, MD (United States). Division of Non-clinical Science. Office of Science. Center for Tobacco Products
Publication Date:
Research Org.:
Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
OSTI Identifier:
1630013
Grant/Contract Number:  
SC0014664
Resource Type:
Accepted Manuscript
Journal Name:
Oncotarget
Additional Journal Information:
Journal Volume: 9; Journal Issue: 24; Journal ID: ISSN 1949-2553
Publisher:
Impact Journals
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES

Citation Formats

Ng, Hui Wen, Leggett, Carmine, Sakkiah, Sugunadevi, Pan, Bohu, Ye, Hao, Wu, Leihong, Selvaraj, Chandrabose, Tong, Weida, and Hong, Huixiao. Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. United States: N. p., 2018. Web. doi:10.18632/oncotarget.24458.
Ng, Hui Wen, Leggett, Carmine, Sakkiah, Sugunadevi, Pan, Bohu, Ye, Hao, Wu, Leihong, Selvaraj, Chandrabose, Tong, Weida, & Hong, Huixiao. Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. United States. https://doi.org/10.18632/oncotarget.24458
Ng, Hui Wen, Leggett, Carmine, Sakkiah, Sugunadevi, Pan, Bohu, Ye, Hao, Wu, Leihong, Selvaraj, Chandrabose, Tong, Weida, and Hong, Huixiao. Thu . "Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents". United States. https://doi.org/10.18632/oncotarget.24458. https://www.osti.gov/servlets/purl/1630013.
@article{osti_1630013,
title = {Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents},
author = {Ng, Hui Wen and Leggett, Carmine and Sakkiah, Sugunadevi and Pan, Bohu and Ye, Hao and Wu, Leihong and Selvaraj, Chandrabose and Tong, Weida and Hong, Huixiao},
abstractNote = {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.},
doi = {10.18632/oncotarget.24458},
journal = {Oncotarget},
number = 24,
volume = 9,
place = {United States},
year = {2018},
month = {2}
}