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McPhail, B., Tie, Y., Hong, H., Pearce, B.A., Schnackenberg, L.K., Ge, W., Valerio, L.G., Fuscoe, J.C., Tong, W., Buzatu, D.A., et al. (2012) Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-Inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes. Molecules, 17, 3283-3406.
http://dx.doi.org/10.3390/molecules17033383

has been cited by the following article:

• TITLE: Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human α4β2 Nicotinic Acetylcholine Receptor

  AUTHORS: Shu Mao, Hui Wen Ng, Michael Orr, Heng Luo, Hao Ye, Weigong Ge, Weida Tong, Huixiao Hong

  KEYWORDS: Nicotinic Acetylcholine Receptors, Homology Model, Ligand-Receptor Interactions

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.9 No.1, January 28, 2016

  ABSTRACT: Addiction to nicotine, and possibly other tobacco constituents, is a major factor that contributes to the difficulties smokers face when attempting to quit smoking. Amongst the various subtypes of nicotinic acetylcholine receptors (nAChRs), the α4β2 subtype plays an important role in mediating the addiction process. The characterization of human α4β2-ligand binding interactions provides a molecular framework for understanding ligand-receptor interactions, rendering insights into mechanisms of nicotine addiction and may furnish a tool for efficiently identifying ligands that can bind the nicotine receptor. Therefore, we constructed a homology model of human α4β2 nAChR and performed molecular docking and molecular dynamics (MD) simulations to elucidate the potential human α4β2-ligand binding modes for eleven compounds known to bind to this receptor. Residues V96, L97 and F151 of the α4 subunit and L111, F119 and F121 of the β2 subunit were found to be involved in hydrophobic interactions while residues S153 and W154 of the α4 subunit were involved in the formation of hydrogen bonds between the receptor and respective ligands. The homology model and its eleven ligand-bound structures will be used to develop a virtual screening program for identifying tobacco constituents that are potentially addictive.