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基于药效团的筛选和 3D-QSAR 方法在新型 JAK2 抑制剂发现中的应用

Pharmacophore filtering and 3D-QSAR in the discovery of new JAK2 inhibitors.

机构信息

Department of Bioinformatics, Alagappa University, Karaikudi 630003, Tamil Nadu, India.

出版信息

J Mol Graph Model. 2011 Sep;30:186-97. doi: 10.1016/j.jmgm.2011.07.004. Epub 2011 Jul 22.

DOI:10.1016/j.jmgm.2011.07.004
PMID:21831680
Abstract

Janus kinase 2 (JAK2) plays a crucial role in the patho-mechanism of cardiovascular pathologies, myeloproliferative disorders and many other diseases. Thus, effective JAK2 kinase inhibitors may be of significant therapeutic importance. In this study, a pharmacophore mapping studies were undertaken for a series of phenylaminopyrimidines derivatives. A five point pharmacophore with two hydrogen bond donors (D), two hydrogen bond acceptors (A) and one aromatic ring (R) as pharmacophoric features were developed. The pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R²=0.970 for training set compounds. The model generated showed excellent predictive power, with a correlation coefficient of Q²=0.822. The external validation indicated that our QSAR models possessed high predictive powers with r²(0) value of 0.999 and r²(m) value of 0.637 respectively. The model was then employed as 3D search query to screen against public compound libraries (Asinex, TOSLab, Maybride and Binding database) in-order to identify a new scaffold. We have identified thirteen distinct drug-like molecules binding to the JAK2. Interestingly, some of the compounds show activity against JAK2 by PASS biological activity prediction. Hence, these molecules could be potential selective inhibitors of JAK2 that can be experimentally validated and their backbone structural scaffold could serve as building blocks in designing drug-like molecules for JAK2.

摘要

Janus 激酶 2(JAK2)在心血管病理学、骨髓增殖性疾病和许多其他疾病的发病机制中起着至关重要的作用。因此,有效的 JAK2 激酶抑制剂可能具有重要的治疗意义。在这项研究中,我们对一系列苯氨基嘧啶衍生物进行了药效团映射研究。开发了一个具有两个氢键供体(D)、两个氢键受体(A)和一个芳环(R)作为药效团特征的 5 点药效团假说。药效团假设产生了一个具有统计学意义的 3D-QSAR 模型,对于训练集化合物,相关系数为 R²=0.970。生成的模型显示出出色的预测能力,相关系数为 Q²=0.822。外部验证表明,我们的 QSAR 模型具有较高的预测能力,r²(0)值为 0.999,r²(m)值为 0.637。然后,我们将该模型用作 3D 搜索查询,以针对公共化合物库(Asinex、TOSLab、Maybride 和 Binding 数据库)进行筛选,以识别新的支架。我们已经确定了十三个与 JAK2 结合的独特类药性分子。有趣的是,一些化合物通过 PASS 生物活性预测显示出对 JAK2 的活性。因此,这些分子可能是 JAK2 的潜在选择性抑制剂,可以通过实验验证,并且它们的骨架结构支架可以用作设计 JAK2 类药性分子的构建块。

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