Competitive docking model for prediction of the human nicotinic acetylcholine receptor ?7 binding of tobacco constituents [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 612.3 Digestion

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2018

Mô tả vật lý: Size: p. 16899-16916 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 261913

 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 ?7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR ?7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR ?7. 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 ?7 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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