Taha Mutasem O, Qandil Amjad M, Zaki Dhia D, AlDamen Murad A
Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
Eur J Med Chem. 2005 Jul;40(7):701-27. doi: 10.1016/j.ejmech.2004.10.014.
The flexibility of activated factor X (fXa) binding site was assessed employing ligand-based pharmacophor modeling combined with genetic algorithm (GA)-based QSAR modeling. Four training subsets of wide structural diversity were selected from a total of 199 direct fXa inhibitors and were employed to generate different fXa pharmacophoric hypotheses using CATALYST software over two subsequent stages. In the first stage, high quality binding models (hypotheses) were identified. However, in the second stage, these models were refined by applying variable feature weight analysis to assess the relative significance of their features in the ligand-target affinity. The binding models were validated according to their coverage (capacity as a three-dimensional (3D) database search queries) and predictive potential as three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Subsequently, GA and multiple linear regression (MLR) analysis were employed to construct different QSAR models from high quality pharmacophores and explore the statistical significance of combination models in explaining bioactivity variations across 199 fXa inhibitors. Three orthogonal pharmacophoric models emerged in the optimal QSAR equation suggesting they represent three binding modes accessible to ligands in the binding pocket within fXa.
采用基于配体的药效团建模结合基于遗传算法（GA）的定量构效关系（QSAR）建模，评估活化因子X（fXa）结合位点的灵活性。从总共199种直接fXa抑制剂中选择了四个具有广泛结构多样性的训练子集，并在两个后续阶段使用CATALYST软件生成不同的fXa药效团假设。在第一阶段，识别出高质量的结合模型（假设）。然而，在第二阶段，通过应用可变特征权重分析来评估其特征在配体-靶点亲和力中的相对重要性，对这些模型进行了优化。根据结合模型的覆盖范围（作为三维（3D）数据库搜索查询的能力）和作为三维定量构效关系（3D-QSAR）模型的预测潜力对其进行验证。随后，采用遗传算法和多元线性回归（MLR）分析，从高质量的药效团构建不同的QSAR模型，并探索组合模型在解释199种fXa抑制剂生物活性变化方面的统计学意义。在最优QSAR方程中出现了三个正交的药效团模型，表明它们代表了fXa结合口袋中配体可及的三种结合模式。
