Ghaffar Ammarah, Batool Sidra, Mushtaq Gohar, Kamal Muhammad A
Department of Biosciences, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad, 44000, Pakistan.
Department of Biochemistry, College of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Curr Comput Aided Drug Des. 2017;13(3):222-233. doi: 10.2174/1573409913666170309144722.
Adenosine-Monophosphate-Activated protein kinase (AMPK) is a conserved kinase that plays an important role in maintaining the homeostasis of cells. AMPK activation has a positive impact on treatment of diseases such as diabetes, obesity and cancer as well. This observation led to the development of AMPK activators. Certain naturally occurring compounds have also been known to activate AMPK.
In this study, we retrieved the AMPK activators that include chemical drugs, xenobiotics and natural compounds and analyzed their interactions with AMPK via docking studies. Using this ligand dataset, a pharmacophore model was generated based upon ligand-based pharmacophore modeling strategy. The generated pharmacophore model was used to screen a library of ZINC database. The new hits which share the properties of our pharmacophore model were further analyzed via docking studies.
This study led to the identification of new chemical compounds which has the potential to activate AMPK. Even some of the screened hits showed better binding energies as compared to that of the ligand dataset used thus having the potential to activate AMPK more efficiently. The promising hits obtained after virtual screening of ZINC database were also checked against the Lipinski's rule of five.
Compound 7 out of the 10 compounds showed best binding energies even more efficient than the ligand dataset itself.
单磷酸腺苷激活蛋白激酶（AMPK）是一种保守的激酶，在维持细胞内稳态中发挥重要作用。AMPK激活对糖尿病、肥胖症和癌症等疾病的治疗也有积极影响。这一发现促使了AMPK激活剂的研发。某些天然存在的化合物也已知能激活AMPK。
在本研究中，我们检索了包括化学药物、外源性物质和天然化合物在内的AMPK激活剂，并通过对接研究分析它们与AMPK的相互作用。使用该配体数据集，基于基于配体的药效团建模策略生成了一个药效团模型。生成的药效团模型用于筛选锌数据库的文库。通过对接研究进一步分析了具有我们药效团模型特性的新命中物。
本研究鉴定出了具有激活AMPK潜力的新化合物。甚至一些筛选出的命中物与所使用的配体数据集相比显示出更好的结合能，因此有可能更有效地激活AMPK。对锌数据库进行虚拟筛选后获得的有前景的命中物也根据Lipinski的五规则进行了检查。
10种化合物中的化合物7显示出最佳结合能，甚至比配体数据集本身更有效。
