Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents
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Hui Wen Ng1,*, Carmine Leggett2,*, Sugunadevi Sakkiah1,*, Bohu Pan1, Hao Ye1, Leihong Wu1, Chandrabose Selvaraj1, Weida Tong1 and Huixiao Hong1
1Division of Bioinformatics and Biostatistics, Office of Research, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
2Division of Non-clinical Science, Office of Science, Center for Tobacco Products, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
*These authors made equal contributions to the work
Huixiao Hong, email: email@example.com
Keywords: prediction; tobacco constituents; addiction; molecular docking; nicotinic acetylcholine receptor
Received: August 28, 2017 Accepted: February 01, 2018 Epub: February 08, 2018 Published: March 30, 2018
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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