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Prediction of metabolism in cytochrome P450s remains to be a crucial yetchallenging topic in discovering and designing drugs, agrochemicals andnutritional supplements. The problem is challenging because the rate of P450metabolism depends upon both the intrinsic chemical reactivity of the site andthe protein-ligand geometry that is energetically accessible in the active siteof a given P450 isozyme. We have addressed this problem using a two-levelscreening system. The first level implements an empirical QSAR-based scoringfunction employing the local chemical motifs to characterize the intrinsicreactivity. The second level uses molecular docking and molecular mechanics toaccount for the geometrical effects, including induced-fit effects in theprotein which can be very important in P450 interactions with ligands. Thisapproach has achieved high accuracy for both the P450 3A4 and 2D6 isoforms. Inidentifying at least one metabolic site in the top two ranked positions, theprediction rate can reach as high as 92.7% for the test set of isoform 3A4. Forthe 2D6 isoform, 100% accuracy is achieved on this basic evaluation metric,and, because this active site is considerably smaller and more selective than3A4, very high precision is attained for full prediction of all metabolicsites. The method also requires considerably less CPU time than our previousefforts, which involved a large number of expensive simulations for each ligandto be evaluated. After screening using the empirical score function, only a fewbest candidates are left for each ligand, making the number of necessaryestimations in the second level very small, which significantly reduces thecomputation time.

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