Modeling of cytochrome P450 metabolism with quantitative structure activity relationship and docking

Abstract

To address these challenges we have created Docking-Regioselectivity-Predictor (DR-Predictor) and Docking-Inhibition-Predictor (DI-Predictor) to predict regioselectivity and inhibition of CYP isozyme ligands. In both models, the effective docking program Autodock Vina is applied for the interaction between CYP and ligand. Interaction descriptors from multiple docked poses are collected for each ligand. Reactivity calculation is approximated by the 2D method SMARTCyp and various physically meaningful descriptors from the semi-empirical program MOPAC. Multiple instance learning algorithms have been applied to integrate descriptors from both interaction and reactivity. MIRank, the multiple instance ranking algorithm, has been applied to rank the sites of metabolism over the non-metabolism sites. Multiple instance classification algorithms such as Multiple Instance Kernel SVM (MIK-SVM) are used for classify inhibitors versus non-inhibitors. Both DR-Predictor and DI-Predictor models achieve high performance, compared to other methods. Based on our integrated framework, we also explore approaches such as elastic network modeling to model highly flexible structure of CYP 3A4. Regioselectivity models based on significantly different conformations are successfully built.