Abstract
Metabolism is one of the prime reasons where most of drugs fail to accomplish their clinical trials. The enzyme CYP3A4, which belongs to the superfamily of cytochrome P450 enzymes (CYP), helps in the metabolism of a large number of drugs in the body. The enzyme CYP3A4 catalyzes oxidative chemical processes and shows a very broad range of ligand specificity. The understanding of the compound's structure where oxidation would take place is crucial for the successful modification of molecules to avoid unwanted metabolism and to increase its bioavailability. For this reason, it is required to know the site of metabolism (SOM) of the compounds, where compounds undergo enzymatic oxidation. It can be identified by predicting the accessibility of the substrate's atom toward oxygenated Fe atom of heme in a CYP protein. The CYP3A4 enzyme is highly flexible and can take significantly different conformations depending on the ligand with which it is being bound. To predict the accessibility of substrate atoms to the heme iron, conventional protein-rigid docking methods failed due to the high flexibility of the CYP3A4 protein. Herein, we demonstrated and compared the ability of the Glide extra precision (XP) and Induced Fit docking (IFD) tool of Schrodinger software suite to reproduce the binding mode of co-crystallized ligands into six X-ray crystallographic structures. We extend our studies toward the prediction of SOM for compounds whose experimental SOM is reported but the ligand-enzyme complex crystal structure is not available in the Protein Data Bank (PDB). The quality and accuracy of Glide XP and IFD was determined by calculating RMSD of docked ligands over the corresponding co-crystallized bound ligand and by measuring the distance between the SOM of the ligand and Fe atom of heme. It was observed that IFD reproduces the exact binding mode of available co-crystallized structures and correctly predicted the SOM of experimentally reported compounds. Our approach using IFD with multiple conformer structures of CYP3A4 will be one of the effective methods for SOM prediction.