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

Background: 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. 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 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 Km 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.