Showing papers by "Guixia Liu published in 2009"
TL;DR: In this paper, the authors investigate the unbinding pathways of two selective ERβ ligands from the binding pocket of both ERα and ERβ, which demonstrated that the pathway between the H11 helix and the H7∼H8 loop was the most probable for ligand escaping.
Abstract: Estrogen receptors (ER) belong to the nuclear receptor superfamily, and two subtypes, ERα and ERβ, have been identified to date. The differentiated functions and receptor expressions of ERα and ERβ made it attracted to discover subtype-specified ligands with high selectivity. However, these two subtypes are highly homologous and only two residues differ in the ligand binding pocket. Therefore, the mechanism of ligand selectivity has become an important issue in searching selective ligands of ER subtypes. In this study, steered molecular dynamics simulations were carried out to investigate the unbinding pathways of two selective ERβ ligands from the binding pocket of both ERα and ERβ, which demonstrated that the pathway between the H11 helix and the H7∼H8 loop was the most probable for ligand escaping. Then potentials of mean force for ligands unbinding along this pathway were calculated in order to gain insights into the molecular basis for energetics of ligand unbinding and find clues of ligand selectivi...
47 citations
TL;DR: Two chemical function-based pharmacophore models of selective κ-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase and were shown to be able to identify highly potent λ-agonists within a certain range, and satisfactory enrichments were achieved.
Abstract: Two chemical function-based pharmacophore models of selective κ-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent κ-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel κ-agonists.
13 citations