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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

Chemical structures of S6K1 kinase inhibitors in the training set together with their biological activity data (IC50) for HipHop run
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Related In: Results  -  Collection


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Fig1: Chemical structures of S6K1 kinase inhibitors in the training set together with their biological activity data (IC50) for HipHop run

Mentions: The HipHop algorithm implemented in the Accelrys Discovery Studio 3.1 program package (Accelrys Inc., San Diego, CA) was employed for pharmacophore modeling. Eight S6K1 inhibitors (A1–A8), based on a wide range of biological activities and structural diversity, were chosen to form the training set (Fig. 1). Compound A1 was selected as “reference compound”, and their “principal” and “MaxOmitFeat” values were assigned as 2 and 0, respectively. The other compounds (A2–A8) of the training set were set to 1 for “Principal” and “MaxOmitFeat” values. The “minimum Interfeature Distance” value was set to 2.97 Å. Five features (hydrogen bond acceptor, hydrogen bond donor, hydrophobic, hydrophobic-aliphatic and ring-aromatic) were initially selected and used for pharmacophore generation. The parameters “Min” and “Max” of the hydrophobic feature were defined as 1 and 5, respectively. All the other parameters were kept at their default values.Fig. 1


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)

Chemical structures of S6K1 kinase inhibitors in the training set together with their biological activity data (IC50) for HipHop run
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Chemical structures of S6K1 kinase inhibitors in the training set together with their biological activity data (IC50) for HipHop run
Mentions: The HipHop algorithm implemented in the Accelrys Discovery Studio 3.1 program package (Accelrys Inc., San Diego, CA) was employed for pharmacophore modeling. Eight S6K1 inhibitors (A1–A8), based on a wide range of biological activities and structural diversity, were chosen to form the training set (Fig. 1). Compound A1 was selected as “reference compound”, and their “principal” and “MaxOmitFeat” values were assigned as 2 and 0, respectively. The other compounds (A2–A8) of the training set were set to 1 for “Principal” and “MaxOmitFeat” values. The “minimum Interfeature Distance” value was set to 2.97 Å. Five features (hydrogen bond acceptor, hydrogen bond donor, hydrophobic, hydrophobic-aliphatic and ring-aromatic) were initially selected and used for pharmacophore generation. The parameters “Min” and “Max” of the hydrophobic feature were defined as 1 and 5, respectively. All the other parameters were kept at their default values.Fig. 1

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