Dai Hao, Xu Qin, Xiong Yi, Liu Wei-Lin, Wei Dong-Qing
Room 4-321, Life Science Building, Shanghai Jiaotong University, 800 Dongchuan Road, 200240, Minhang District, Shanghai, China.
Curr Drug Metab. 2016;17(7):673-80. doi: 10.2174/1389200217666160513144551.
In drug metabolism reactions, it has become increasingly important to measure Michaelis constants (Km), which are used for a variety of purposes, including identification of enzymes involved in drug metabolism, prediction of drug-drug interactions, etc. Cytochrome P450s (CYPs) comprise a super family of major human enzymes responsible for drug metabolism. Hence, computational prediction of Km in CYP-mediated reactions facilitates drug development in an efficient and economical way.
In this study, we firstly constructed a large dataset of ten CYP isoforms associated with 169 binding substrates, and 210 experimental Km values in CYP-mediated reactions. To predict Km of substrates metabolized by various CYP isoforms, we developed a general prediction model by using resilient back-propagation neutral network algorithm, based on the structural and physicochemical properties of the substrates and the metabolic specificity of the enzymes.
The predictive Km values achieve a squared cross-validation correlation coefficients (Q2) of 0.73 with the experimental values, which is better than that of the existing models. Moreover, our model can predict Kmvalues of the compounds metabolized by a wide range of CYP isoforms.
This tool will be useful in large-scale drug screening studies for CYP enzymes and helpful in the drug design and development.
在药物代谢反应中，测定米氏常数（Km）变得越来越重要，其可用于多种目的，包括鉴定参与药物代谢的酶、预测药物相互作用等。细胞色素P450（CYP）是负责药物代谢的主要人类酶的一个超家族。因此，CYP介导反应中Km的计算预测有助于以高效且经济的方式进行药物开发。
在本研究中，我们首先构建了一个大型数据集，包含与169种结合底物相关的十种CYP同工型以及CYP介导反应中的210个实验Km值。为了预测各种CYP同工型代谢的底物的Km，我们基于底物的结构和物理化学性质以及酶的代谢特异性，使用弹性反向传播神经网络算法开发了一个通用预测模型。
预测的Km值与实验值的平方交叉验证相关系数（Q2）达到0.73，优于现有模型。此外，我们的模型可以预测多种CYP同工型代谢的化合物的Km值。
该工具将有助于大规模筛选CYP酶的药物研究，并有助于药物设计与开发。
