Prediction of Cytochrome P450-Mediated Metabolism Using a Combination of QSAR Derived Reactivity and Induced Fit Docking

Abstract

Prediction of metabolism in cytochrome P450s remains to be a crucial yet challenging topic in discovering and designing drugs, agrochemicals and nutritional supplements. The problem is challenging because the rate of P450 metabolism depends upon both the intrinsic chemical reactivity of the site and the protein-ligand geometry that is energetically accessible in the active site of a given P450 isozyme. We have addressed this problem using a two-level screening system. The first level implements an empirical QSAR-based scoring function employing the local chemical motifs to characterize the intrinsic reactivity. The second level uses molecular docking and molecular mechanics to account for the geometrical effects, including induced-fit effects in the protein which can be very important in P450 interactions with ligands. This approach has achieved high accuracy for both the P450 3A4 and 2D6 isoforms. In identifying at least one metabolic site in the top two ranked positions, the prediction rate can reach as high as 92.7% for the test set of isoform 3A4. For the 2D6 isoform, 100% accuracy is achieved on this basic evaluation metric, and, because this active site is considerably smaller and more selective than 3A4, very high precision is attained for full prediction of all metabolic sites. The method also requires considerably less CPU time than our previous efforts, which involved a large number of expensive simulations for each ligand to be evaluated. After screening using the empirical score function, only a few best candidates are left for each ligand, making the number of necessary estimations in the second level very small, which significantly reduces the computation time.