RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.
RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.
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DOI：
10.1021/ci2000488
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发表时间：
2011-07-25
影响因子：
5.6
通讯作者：
Breneman CM
中科院分区：
文献类型：
--
作者：
Zaretzki J;Bergeron C;Rydberg P;Huang TW;Bennett KP;Breneman CM
本文介绍了一种新的用于生成p450介导的药物样化合物代谢预测模型的计算机方法——RegioSelectivity-Predictor （RS-Predictor）。在这种方法中，潜在代谢位点（SOMs）被表示为“代谢团”：这是一个描述拓扑和量子化学描述符的层次组合的概念，用于表示潜在代谢反应位点的反应性。RS-Predictor建模包括使用代谢团描述符和多实例排序（MIRank）来生成一个优化的描述符权重向量，该向量编码训练集中所有情况下的区域选择性趋势。由此产生的与途径无关的同工酶特异性区域选择性模型可用于预测潜在的代谢负荷。在目前的工作中，为一组394个CYP 3A4底物生成了交叉验证的RS-Predictor模型，作为该方法的原理证明。然后使用秩聚合将每个底物独立生成的预测合并为单个共识预测。结果表明，一致的RS-Predictor模型能够可靠地识别出78%底物中位于前两个位置的至少一个观察到的代谢位点。RS-Predictor和先前描述的区域选择性预测方法之间的比较揭示了如何比较硅代谢物预测方法的新见解。
This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as “metabolophores”: A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent,i isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.