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3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: Comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments

机译:蛋白酪氨酸磷酸酶1B抑制剂的3D QSAR研究:从基于构象异构体的比对中获得的3D QSAR模型之间的质量和可预测性的比较

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摘要

A set of 65 flexible peptidomimetic competitive inhibitors ( 52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis ( CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformerbased alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino) propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r(2) (q(2)) = 0.708, non-cross-validated r(2) = 0.902, standard error of estimate (s) = 0.165, and F = 202.553 and the optimal CoMSIA model with q(2) = 0.440, r(2) = 0.799, s = 0.192, and F = 117.782. These models also showed the best test set prediction for the 13 compounds with predictive r(2) values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA > GMCBA in terms of the values of q(2), r(2), and predictive r(2), they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA > DCBA > GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.
机译:一组65种蛋白质模拟酪氨酸磷酸酶1B(PTP1B)的柔性拟肽竞争性抑制剂(训练组52种,测试组13种)已用于比较3D定量构效关系(QSAR)的质量和预测能力。分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)模型用于三种最常用的基于构象体的比对,即共结晶基于构象体的比对(CCBA),对接的基于构象体的比对(DCBA)和全局最小能构象基于比对(GMCBA)。 5-[(2S)-2-({(2S)-2-[(叔丁氧基羰基)氨基] -3-苯基丙酰基}氨基)3-氧代-3-戊基氨基)丙基的这三个构象丙基-2-(通过与PTP1B(PDB ID:1JF7)共结晶的复合物的X射线结构,对接研究以及通过模拟退火获得的整体最小值,获得了羧基甲氧基)苯甲酸(化合物编号66)。在使用上述三种比对方法开发的3D QSAR模型中,CCBA为交叉验证的r(2)(q(2))= 0.708,非交叉验证的r(2)的训练集提供了最佳的预测CoMFA模型。 = 0.902,估计的标准误差(s)= 0.165,F = 202.553,q(2)= 0.440,r(2)= 0.799,s = 0.192,F = 117.782的最优CoMSIA模型。这些模型还显示了13种化合物的最佳测试集预测,预测r(2)值分别为0.706和0.683。尽管使用其他两个比对得出的QSAR模型也根据q(2),r(2)和预测性r(2)的值以DCBA> GMCBA的顺序生成了统计上可接受的模型,但它们不如相应的使用CCBA导出的模型。因此,用于3D QSAR模型开发的比对选择的优先顺序可以是CCBA> DCBA> GMCBA,并且从CoMFA和CoMSIA等高线图获得的信息可能对设计特定的PTP1B抑制剂有用。

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