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基于分子对接、比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)的检查点激酶Weel抑制剂的三维定量构效关系(3D-QSAR)研究

3D-QSAR studies of Checkpoint Kinase Weel inhibitors based on molecular docking, CoMFA and CoMSIA.

作者信息

Yi Ping, Fang Xin, Qiu Minghua

机构信息

State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650204, China.

出版信息

Eur J Med Chem. 2008 May;43(5):925-38. doi: 10.1016/j.ejmech.2007.06.021. Epub 2007 Aug 14.

Abstract

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on 97 4-phenylpyrrolo[3,4-c]carbazole-1,3(2H,6H)-dione inhibitors, based on molecular docking scores obtained by using GOLD 3.1, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The docking results provided a reliable conformational alignment scheme for the 3D-QSAR model. Based on the docking conformations and alignments, highly predictive CoMFA and CoMSIA were obtained with cross-validated q(2) value of 0.828 and 0.796, respectively, and non-cross-validated partial least-squares (PLS) analysis with the optimum components of five showed a conventional r(2) of 0.962 and 0.949, respectively. The predictive ability was validated by compounds that were not included in the training set. Furthermore, the CoMFA and CoMSIA model plots were mapped back to the binding sites of Checkpoint Kinase Weel, to get a better understanding of vital interactions between the inhibitors and Weel kinase. As a result, we have identified some key features in the 4-phenylpyrrolo[3,4-c]carbazole-1,3(2H,6H)-diones responsible for the Weel inhibitory activity that may be used to design more potent 4-phenylpyrrolo[3,4-c]carbazole-1,3(2H,6H)-diones and predict their activity prior to synthesis.

摘要

基于使用GOLD 3.1获得的分子对接分数、比较分子场分析(CoMFA)和比较分子相似性指数(CoMSIA),对97种4-苯基吡咯并[3,4-c]咔唑-1,3(2H,6H)-二酮抑制剂进行了三维定量构效关系(3D-QSAR)研究。对接结果为3D-QSAR模型提供了可靠的构象比对方案。基于对接构象和比对,获得了预测性较高的CoMFA和CoMSIA,交叉验证的q(2)值分别为0.828和0.796,非交叉验证的偏最小二乘法(PLS)分析在最佳组分为5时,常规r(2)值分别为0.962和0.949。通过未包含在训练集中的化合物验证了预测能力。此外,将CoMFA和CoMSIA模型图映射回检查点激酶Weel的结合位点,以更好地理解抑制剂与Weel激酶之间的重要相互作用。结果,我们确定了4-苯基吡咯并[3,4-c]咔唑-1,3(2H,6H)-二酮中一些负责Weel抑制活性的关键特征,这些特征可用于设计更有效的4-苯基吡咯并[3,4-c]咔唑-1,3(2H,6H)-二酮,并在合成前预测其活性。

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