Université Bordeaux 1, Talence Cedex, France.
SAR QSAR Environ Res. 2012 Jan;23(1-2):87-109. doi: 10.1080/1062936X.2011.636380. Epub 2011 Dec 8.
To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.
为了获得细胞色素 P450 1A1 和 1B1 生物活化过程的化学线索,我们进行了一些定量构效关系研究,这些研究基于这些酶对多环芳烃和杂环芳烃化合物代谢活化的细胞实验。首先,使用细胞色素 1A2 模板进行同源建模构建了细胞色素 1A1 和 1B1 的 3D 结构。使用这些结构,用 LigandFit 和 CDOCKER 算法对接了 32 种配体,包括杂环芳烃、多环芳烃和相应的二醇。结合模式分析强调了疏水相互作用和细胞色素氨基酸与对接分子之间氢键网络的重要性。最后,为每种酶开发并比较了多元线性回归和人工神经网络 QSAR 模型。这些统计模型强调了电子、结构和能量描述符在代谢活化过程中的重要性,可用于配体数据库的虚拟筛选。在 P450 1A1 的情况下,使用人工神经网络分析获得了最佳模型,其 r(2)值为 0.66,外部预测[Formula: see text]值为 0.73。对于 P450 1B1,人工神经网络分析给出了一个更稳健的模型,其 r(2)值为 0.73,外部预测[Formula: see text]值为 0.59。