首页> 外文期刊>Journal of Medicinal Chemistry >How to deal with low-resolution target structures: Using SAR, ensemble docking, hydropathic analysis, and 3D-QSAR to definitively map the αβ-tubulin colchicine site
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How to deal with low-resolution target structures: Using SAR, ensemble docking, hydropathic analysis, and 3D-QSAR to definitively map the αβ-tubulin colchicine site

机译:如何处理低分辨率目标结构:使用SAR,整体对接,亲水分析和3D-QSAR明确绘制αβ-微管蛋白秋水仙碱位点

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摘要

αβ-Tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q~2 of 0.616, r~2 of 0.949, and r~2 pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis, and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 ? available for αβ-tubulin.
机译:对我们先前测试的抗增殖活性的四个支架的αβ-管蛋白秋水仙碱位点抑制剂(CSI)进行建模,以更好地了解它们对微管的作用。通过利用吡咯子集的SAR和HINT评分构建的对接模型,指导了所有59种化合物的整体对接。对该构象集和两个结构知识逐渐减少的变体进行了CoMFA,CoMFA + HINT和CoMSIA 3D-QSAR分析。 CoMFA + HINT模型(对接比对)显示出最佳的统计数据:q〜2值为0.616,r〜2为0.949,r〜2 pred(内部测试集)为0.755。使用3D-QSAR模型评估了来自8个支架的24个CSI的外部(在其他实验室中进行了测试),该3D-QSAR模型正确地将其活动趋势按7/8支架的CoMFA + HINT排序(对于CoMFA为8/8)。 SAR,整体对接,亲水分析和3D-QSAR的组合提供了一个原子尺度的秋水仙碱位点模型,其目标结构分辨率远高于〜3.6?可用于αβ-微管蛋白。

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