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采用CoMFA和CoMSIA方法对含硼二肽作为蛋白酶体抑制剂进行的3D-QSAR研究。

3D-QSAR studies of boron-containing dipeptides as proteasome inhibitors with CoMFA and CoMSIA methods.

作者信息

Zhu Yong-Qiang, Lei Meng, Lu Ai-Jun, Zhao Xin, Yin Xiao-Jin, Gao Qing-Zhi

机构信息

Jiangsu Simcere Pharmaceutical Research Institute, No. 699-18 Xuan Wu Avenue, Xuan Wu District, Nanjing 210042, PR China.

出版信息

Eur J Med Chem. 2009 Apr;44(4):1486-99. doi: 10.1016/j.ejmech.2008.07.019. Epub 2008 Jul 24.

Abstract

Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed for a series of dipeptide boronate proteasome inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. A training set containing 46 molecules served to establish the models. The optimum CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients (q(2)) of 0.676 and 0.630 and conventional coefficients (r(2)) of 0.989 and 0.956, respectively. The predictive capacities of both models were successfully validated by calculating a test set of 13 molecules that were not included in the training set. The predicted correlation coefficients (r(2)(pred)) of CoMFA and CoMSIA are 0.963 and 0.919, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of beta5 subunit of 20S proteasome, which suggests that the 3D-QSAR models constructed in this paper can be used to guide the development of novel dipeptide boronate inhibitors of 20S proteasome.

摘要

使用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)技术,对一系列二肽硼酸酯蛋白酶体抑制剂进行了三维定量构效关系(3D-QSAR)研究。一个包含46个分子的训练集用于建立模型。为训练集获得的最佳CoMFA和CoMSIA模型均具有统计学意义,交叉验证系数(q(2))分别为0.676和0.630,常规系数(r(2))分别为0.989和0.956。通过计算一个不包含在训练集中的13个分子的测试集,成功验证了这两个模型的预测能力。CoMFA和CoMSIA的预测相关系数(r(2)(pred))分别为0.963和0.919。CoMFA和CoMSIA场等值线图与20S蛋白酶体β5亚基结合口袋的结构特征吻合良好,这表明本文构建的3D-QSAR模型可用于指导新型20S蛋白酶体二肽硼酸酯抑制剂的开发。

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