使用QSAR衍生反应性和诱导拟合对接的组合预测细胞色素P450-Mediated代谢
Prediction of Cytochrome P450-Mediated Metabolism Using a Combination of QSAR Derived Reactivity and Induced Fit Docking
摘要：
细胞色素P450代谢的预测仍然是发现和设计药物、农用化学品和营养补充剂的关键而具有挑战性的课题。这个问题是具有挑战性的，因为P450代谢的速率取决于该位点的固有化学反应性和给定P450同工酶的活性位点中能量可及的蛋白质-配体几何结构。我们使用两级筛选系统解决了这个问题。第一级实现了一个经验的QSAR为基础的评分功能，采用当地的化学图案来表征的内在反应性。第二个层次使用分子对接和分子力学来解释几何效应，包括蛋白质中的诱导拟合效应，这在P450与配体的相互作用中非常重要。这种方法对P450 3A 4和2D 6亚型都实现了高准确度。在鉴定前两个排名位置中的至少一个代谢位点时，对于同种型3A 4的测试集，预测率可高达92.7%。对于2D 6同种型，在该基本评价指标上实现了100%的准确度，并且由于该活性位点比3A 4小得多并且更具选择性，因此对于所有代谢位点的完全预测实现了非常高的精度。该方法也需要比我们以前的努力，其中涉及大量的昂贵的模拟每个配体进行评估的CPU时间少得多。在使用经验评分函数进行筛选之后，对于每个配体，仅留下几个最佳候选者，使得第二级中的必要估计的数量非常小，这显著地减少了计算时间。
英文摘要：
Prediction of metabolism in cytochrome P450s remains to be a crucial yet challenging topic in discovering and designing drugs, agrochemicals and nutritional supplements. The problem is challenging because the rate of P450 metabolism depends upon both the intrinsic chemical reactivity of the site and the protein-ligand geometry that is energetically accessible in the active site of a given P450 isozyme. We have addressed this problem using a two-level screening system. The first level implements an empirical QSAR-based scoring function employing the local chemical motifs to characterize the intrinsic reactivity. The second level uses molecular docking and molecular mechanics to account for the geometrical effects, including induced-fit effects in the protein which can be very important in P450 interactions with ligands. This approach has achieved high accuracy for both the P450 3A4 and 2D6 isoforms. In identifying at least one metabolic site in the top two ranked positions, the prediction rate can reach as high as 92.7% for the test set of isoform 3A4. For the 2D6 isoform, 100% accuracy is achieved on this basic evaluation metric, and, because this active site is considerably smaller and more selective than 3A4, very high precision is attained for full prediction of all metabolic sites. The method also requires considerably less CPU time than our previous efforts, which involved a large number of expensive simulations for each ligand to be evaluated. After screening using the empirical score function, only a few best candidates are left for each ligand, making the number of necessary estimations in the second level very small, which significantly reduces the computation time.
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