Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería en Bioinformática, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile.
J Comput Aided Mol Des. 2011 Apr;25(4):349-69. doi: 10.1007/s10822-011-9425-1. Epub 2011 Apr 13.
We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC(50) values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.
我们对 3-氟-4-(吡咯并[2,1-f][1,2,4]三嗪-4-基氧基)苯胺(FPTA)、3-氟-4-(1H-吡咯并[2,3-b]吡啶-4-基氧基)苯胺(FPPA)和 4-(4-氨基-2-氟苯氧基)-2-吡啶基胺(AFPP)与 c-Met 激酶的复合物进行了对接,以研究这些抑制剂的取向和优选的活性构象。该研究针对一组结构和活性均有变化的 103 种化合物进行了研究。对接有助于分析对研究化合物具有高抑制活性的分子特征。此外,通过定量构效关系(QSAR)方法:比较分子相似性分析(CoMSIA)和拓扑向量的多元线性回归(MLR),获得了 c-Met 激酶抑制剂的预测生物学活性,以 IC(50)值表示。最佳的 CoMSIA 模型包括立体、静电、疏水和氢键供体场;此外,我们发现了一个包含二维自相关描述符、GETAWAY 描述符(GETAWAY:几何、拓扑和原子权重组装)、基于片段的极性表面积(PSA)和 MlogP 的预测模型。交叉验证相关系数和拟合相关系数等统计参数验证了获得的预测模型对 76 种化合物的质量。此外,这些模型还适当地预测了 25 种未包含在训练集中的化合物。