Improved Prediction of Michaelis Constants in CYP450-Mediated Reactions by Resilient Back Propagation Algorithm

Title:Improved Prediction of Michaelis Constants in CYP450-Mediated Reactions by Resilient Back Propagation Algorithm

Volume: 17 Issue: 7

Author(s): Hao Dai, Qin Xu, Yi Xiong, Wei-Lin Liu and Dong-Qing Wei

Affiliation:

Keywords: Cytochrome P450, drug metabolism, genetic algorithm, michaelis constant, neural network, resilient back propagation.

Abstract: Background: In drug metabolism reactions, it has become increasingly important to measure Michaelis constants (K_m), 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 K_m in CYP-mediated reactions facilitates drug development in an efficient and economical way.

Methods: 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.

Results: The predictive K_m 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 K_m values of the compounds metabolized by a wide range of CYP isoforms.

Conclusion: This tool will be useful in large-scale drug screening studies for CYP enzymes and helpful in the drug design and development.

Cite this article as:

Dai Hao, Xu Qin, Xiong Yi, Liu Wei-Lin and Wei Dong-Qing, Improved Prediction of Michaelis Constants in CYP450-Mediated Reactions by Resilient Back Propagation Algorithm, Current Drug Metabolism 2016; 17 (7) . https://dx.doi.org/10.2174/1389200217666160513144551