Li Jianing, Schneebeli Severin T, Bylund Joseph, Farid Ramy, Friesner Richard A
Department of Chemistry, Columbia University, New York, NY.
J Chem Theory Comput. 2011 Nov 8;7(11):3829-3845. doi: 10.1021/ct200462q.
Accurate prediction of drug metabolism is crucial for drug design. Since a large majority of drugs metabolism involves P450 enzymes, we herein describe a computational approach, IDSite, to predict P450-mediated drug metabolism. To model induced-fit effects, IDSite samples the conformational space with flexible docking in Glide followed by two refinement stages using the Protein Local Optimization Program (PLOP). Sites of metabolism (SOMs) are predicted according to a physical-based score that evaluates the potential of atoms to react with the catalytic iron center. As a preliminary test, we present in this paper the prediction of hydroxylation and O-dealkylation sites mediated by CYP2D6 using two different models: a physical-based simulation model, and a modification of this model in which a small number of parameters are fit to a training set. Without fitting any parameters to experimental data, the Physical IDSite scoring recovers 83% of the experimental observations for 56 compounds with a very low false positive rate. With only 4 fitted parameters, the Fitted IDSite was trained with the subset of 36 compounds and successfully applied to the other 20 compounds, recovering 94% of the experimental observations with high sensitivity and specificity for both sets.
准确预测药物代谢对于药物设计至关重要。由于绝大多数药物代谢涉及细胞色素P450酶，我们在此描述一种计算方法——IDSite，用于预测P450介导的药物代谢。为了模拟诱导契合效应，IDSite在Glide中通过柔性对接对构象空间进行采样，随后使用蛋白质局部优化程序（PLOP）进行两个优化阶段。根据基于物理的评分预测代谢位点（SOMs），该评分评估原子与催化铁中心反应的潜力。作为初步测试，我们在本文中使用两种不同模型展示了对CYP2D6介导的羟基化和O-脱烷基化位点的预测：基于物理的模拟模型，以及对该模型的一种修改版本，其中少量参数拟合到一个训练集。在未将任何参数拟合到实验数据的情况下，物理IDSite评分在56种化合物中恢复了83%的实验观察结果，假阳性率非常低。仅使用4个拟合参数，拟合IDSite在36种化合物的子集上进行训练，并成功应用于其他20种化合物，在两组中均以高灵敏度和特异性恢复了94%的实验观察结果。
