Xie Aihua, Odde Srinivas, Prasanna Sivaprakasam, Doerksen Robert J
Department of Medicinal Chemistry, University of Mississippi, University, MS 38677-1848, USA.
J Comput Aided Mol Des. 2009 Jul;23(7):431-48. doi: 10.1007/s10822-009-9278-z. Epub 2009 May 29.
One of the most promising anticancer and recent antimalarial targets is the heterodimeric zinc-containing protein farnesyltransferase (FT). In this work, we studied a highly diverse series of 192 Abbott-initiated imidazole-containing compounds and their FT inhibitory activities using 3D-QSAR and docking, in order to gain understanding of the interaction of these inhibitors with FT to aid development of a rational strategy for further lead optimization. We report several highly significant and predictive CoMFA and CoMSIA models. The best model, composed of CoMFA steric and electrostatic fields combined with CoMSIA hydrophobic and H-bond acceptor fields, had r (2) = 0.878, q (2) = 0.630, and r (pred) (2) = 0.614. Docking studies on the statistical outliers revealed that some of them had a different binding mode in the FT active site based on steric bulk and available active site space, explaining why the predicted activities differed from the experimental activities.
最具前景的抗癌及近期抗疟靶点之一是异二聚体含锌蛋白法尼基转移酶(FT)。在本研究中,我们使用3D-QSAR和对接技术,研究了由雅培公司研发的192种高度多样化的含咪唑化合物及其FT抑制活性,以便了解这些抑制剂与FT的相互作用,为进一步优化先导化合物制定合理策略提供帮助。我们报告了几个具有高度显著性和预测性的CoMFA和CoMSIA模型。最佳模型由CoMFA空间场和静电场与CoMSIA疏水场和氢键受体场组合而成,其r (2) = 0.878,q (2) = 0.630,r (pred) (2) = 0.614。对统计异常值的对接研究表明,其中一些化合物基于空间体积和可用活性位点空间,在FT活性位点具有不同的结合模式,这解释了预测活性与实验活性不同的原因。