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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]
+ Show Author Affiliations
  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 Institute 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.
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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
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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.
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@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 = {Thu Feb 08 00:00:00 EST 2018},
month = {Thu Feb 08 00:00:00 EST 2018}
}
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Discovery and Structure−Activity Relationship of Quinuclidine Benzamides as Agonists of α7 Nicotinic Acetylcholine Receptors
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Discovery of novel α7 nicotinic acetylcholine receptor ligands via pharmacophoric and docking studies of benzylidene anabaseine analogs
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journal, December 2013

  • Kombo, David C.; Bencherif, Merouane
  • Journal of Chemical Information and Modeling, Vol. 53, Issue 12
  • DOI: 10.1021/ci400493a

Molecular blueprint of allosteric binding sites in a homologue of the agonist-binding domain of the α7 nicotinic acetylcholine receptor
journal, April 2015

  • Spurny, Radovan; Debaveye, Sarah; Farinha, Ana
  • Proceedings of the National Academy of Sciences, Vol. 112, Issue 19
  • DOI: 10.1073/pnas.1418289112

Discovery of α7-Nicotinic Receptor Ligands by Virtual Screening of the Chemical Universe Database GDB-13
journal, November 2011

  • Blum, Lorenz C.; van Deursen, Ruud; Bertrand, Sonia
  • Journal of Chemical Information and Modeling, Vol. 51, Issue 12
  • DOI: 10.1021/ci200410u

An Unaltered Orthosteric Site and a Network of Long-Range Allosteric Interactions for PNU-120596 in α7 Nicotinic Acetylcholine Receptors
journal, August 2015

An Unusual Pattern of Ligand-Receptor Interactions for theα7 Nicotinic Acetylcholine Receptor, with Implications for the Binding of Varenicline
journal, May 2013

  • Van Arnam, Ethan B.; Blythe, Emily E.; Lester, Henry A.
  • Molecular Pharmacology, Vol. 84, Issue 2
  • DOI: 10.1124/mol.113.085795

Modeling Binding Modes of α7 Nicotinic Acetylcholine Receptor with Ligands: The Roles of Gln117 and Other Residues of the Receptor in Agonist Binding
journal, October 2008

  • Huang, Xiaoqin; Zheng, Fang; Stokes, Clare
  • Journal of Medicinal Chemistry, Vol. 51, Issue 20
  • DOI: 10.1021/jm800607u

Effects of mutations of a glutamine residue in loop D of the α7 nicotinic acetylcholine receptor on agonist profiles for neonicotinoid insecticides and related ligands
journal, September 2002

  • Shimomura, Masaru; Okuda, Hiroshi; Matsuda, Kazuhiko
  • British Journal of Pharmacology, Vol. 137, Issue 2
  • DOI: 10.1038/sj.bjp.0704848

Glide:  A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy
journal, March 2004

  • Friesner, Richard A.; Banks, Jay L.; Murphy, Robert B.
  • Journal of Medicinal Chemistry, Vol. 47, Issue 7
  • DOI: 10.1021/jm0306430

Extra Precision Glide:  Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
journal, October 2006

  • Friesner, Richard A.; Murphy, Robert B.; Repasky, Matthew P.
  • Journal of Medicinal Chemistry, Vol. 49, Issue 21
  • DOI: 10.1021/jm051256o

Glide:  A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening
journal, March 2004

  • Halgren, Thomas A.; Murphy, Robert B.; Friesner, Richard A.
  • Journal of Medicinal Chemistry, Vol. 47, Issue 7
  • DOI: 10.1021/jm030644s

Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery
journal, May 2014

  • Fischer, Marcus; Coleman, Ryan G.; Fraser, James S.
  • Nature Chemistry, Vol. 6, Issue 7
  • DOI: 10.1038/nchem.1954
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