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Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1.

Zhang H, Xiang ML, Liang JY, Zeng T, Zhang XN, Zhang J, Yang SY - Mol. Divers. (2013)

Bottom Line: Discovery of S6K1 inhibitors has thus attracted much attention in recent years.The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors.Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Northwest Normal University, Lanzhou , 730070, Gansu, People's Republic of China, zhanghui123gansu@163.com.

ABSTRACT
S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

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Related in: MedlinePlus

a Best pharmacophore model of S6K1 inhibitors generated by HipHop. b 3D spatial relationship and geometric parameters of Hypo1. c The best HipHop model aligned with one of the most active compounds 1 (IC50  1 nM) in the training set. The features are color coded: orange ring-aromatic, green hydrogen-bond acceptor, magenta hydrogen-bond donor, cyan hydrophobic feature. (Color figure online)
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Fig2: a Best pharmacophore model of S6K1 inhibitors generated by HipHop. b 3D spatial relationship and geometric parameters of Hypo1. c The best HipHop model aligned with one of the most active compounds 1 (IC50 1 nM) in the training set. The features are color coded: orange ring-aromatic, green hydrogen-bond acceptor, magenta hydrogen-bond donor, cyan hydrophobic feature. (Color figure online)

Mentions: Eight inhibitors, containing different scaffolds and activities, were used to generate common pharmacophore features. Finally, a total of ten pharmacophore models were generated by using HipHop algorithm. Figure 2a shows the best pharmacophore model, Hypo1, with four features: one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic feature, and one ring-aromatic feature. The 3D space and distance constraints of these pharmacophore features are presented in Fig. 2b. The hydrophobic feature is far from the centers of ring-aromatic feature, hydrogen bond donor and hydrogen bond acceptor by 5.004, 4.780, and 5.111 Å, respectively. The hydrogen bond donor feature is far from the ring-aromatic feature and hydrogen bond acceptor by 5.757 Å and 3.606 Å. And the centers of the ring-aromatic feature and hydrogen bond acceptor feature are separated by 6.704 Å. Figure 2c shows the most active inhibitor mapped with the pharmacophore features. Clearly, inhibitor A1 is mapped very well (fit value: 3.99) with these features of Hypo1. Furthermore, in order to further validate the established pharmacophore model, all the collected inhibitors (73 compounds) were mapped on Hypo1. The results showed 84.9 % inhibitors were mapped with the features of Hypo1 (fit value >2.5). Taken together, this demonstrates the established pharmacophore model is in line with the features of S6K1 inhibitors.Fig. 2


Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1.

Zhang H, Xiang ML, Liang JY, Zeng T, Zhang XN, Zhang J, Yang SY - Mol. Divers. (2013)

a Best pharmacophore model of S6K1 inhibitors generated by HipHop. b 3D spatial relationship and geometric parameters of Hypo1. c The best HipHop model aligned with one of the most active compounds 1 (IC50  1 nM) in the training set. The features are color coded: orange ring-aromatic, green hydrogen-bond acceptor, magenta hydrogen-bond donor, cyan hydrophobic feature. (Color figure online)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3824193&req=5

Fig2: a Best pharmacophore model of S6K1 inhibitors generated by HipHop. b 3D spatial relationship and geometric parameters of Hypo1. c The best HipHop model aligned with one of the most active compounds 1 (IC50 1 nM) in the training set. The features are color coded: orange ring-aromatic, green hydrogen-bond acceptor, magenta hydrogen-bond donor, cyan hydrophobic feature. (Color figure online)
Mentions: Eight inhibitors, containing different scaffolds and activities, were used to generate common pharmacophore features. Finally, a total of ten pharmacophore models were generated by using HipHop algorithm. Figure 2a shows the best pharmacophore model, Hypo1, with four features: one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic feature, and one ring-aromatic feature. The 3D space and distance constraints of these pharmacophore features are presented in Fig. 2b. The hydrophobic feature is far from the centers of ring-aromatic feature, hydrogen bond donor and hydrogen bond acceptor by 5.004, 4.780, and 5.111 Å, respectively. The hydrogen bond donor feature is far from the ring-aromatic feature and hydrogen bond acceptor by 5.757 Å and 3.606 Å. And the centers of the ring-aromatic feature and hydrogen bond acceptor feature are separated by 6.704 Å. Figure 2c shows the most active inhibitor mapped with the pharmacophore features. Clearly, inhibitor A1 is mapped very well (fit value: 3.99) with these features of Hypo1. Furthermore, in order to further validate the established pharmacophore model, all the collected inhibitors (73 compounds) were mapped on Hypo1. The results showed 84.9 % inhibitors were mapped with the features of Hypo1 (fit value >2.5). Taken together, this demonstrates the established pharmacophore model is in line with the features of S6K1 inhibitors.Fig. 2

Bottom Line: Discovery of S6K1 inhibitors has thus attracted much attention in recent years.The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors.Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

View Article: PubMed Central - PubMed

Affiliation: College of Life Science, Northwest Normal University, Lanzhou , 730070, Gansu, People's Republic of China, zhanghui123gansu@163.com.

ABSTRACT
S6K1 has emerged as a potential target for the treatment for obesity, type II diabetes and cancer diseases. Discovery of S6K1 inhibitors has thus attracted much attention in recent years. In this investigation, a hybrid virtual screening method that involves pharmacophore hypothesis, genetic function approximation (GFA) model, and molecular docking technology has been used to discover S6K1 inhibitors especially with novel scaffolds. The common feature pharmacophore hypothesis and GFA regression model of S6K1 inhibitors were first developed and applied in a virtual screen of the Specs database for retrieving S6K1 inhibitors. Then, the molecular docking method was carried out to re-filter these screened compounds. Finally, 60 compounds with promising S6K1 inhibitory activity were carefully selected and have been handed over to the other group to complete the follow-up compound synthesis (or purchase) and activity test.

Show MeSH
Related in: MedlinePlus