Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction

Structure-based prediction of a compound's potential sites of metabolism (SOMs) mediated by cytochromes P450 (CYPs) is highly advantageous in the early stage of drug discovery. However, the SOMs prediction accuracy could be influenced by several factors. CYP2C9 is one of the major drug metabolizing enzymes in humans and is responsible for the metabolism of ~13% of clinically used drugs. In this study, we systematically evaluated the effects of protein crystal structural models, scoring functions, heme forms, conserved active site water molecule, and protein flexibility on SOMs prediction of CYP2C9 substrates. Our results demonstrated that ChemScore and GlideScore outperformed four other scoring functions including Vina, GoldScore, ChemPLP, and ASP on average. The performance of the crystal structural models with the pentacoordinated heme was generally superior to that of the hexacoordinated iron-oxo heme (referred to as Compound I) models. The inclusion of the conserved active site water molecule improved the prediction accuracy by GlideScore but reduced the accuracy by ChemScore. In addition, the effect of the conserved water on SOMs prediction was found to be dependent on the receptor model and the substrate. We further found that one of snapshots from molecular dynamics simulations on the apo form can improve the prediction accuracy when compared to the crystal structural model.