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

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.