Abstract
CYP3A4 contributes to the metabolism of more than 30% of drugs in clinical use. Predicting the sites of metabolism (SOM) by CYP3A4, as well as the binding modes, for target compounds is important for the design of metabolically more stable drugs. Precisely predicting the structures of CYP3A4–ligand complexes is enormously challenging owing to the high number of conformational possibilities with its numerous binding substrates. We previously described a method for predicting the SOM of carbamazepine by means of docking and molecular dynamics (MD) simulations starting from multiple initial structures. To validate our method, we have now applied it to tolterodine, which is more flexible than carbamazepine. In addition, we evaluated the effectiveness of two methods for selecting the initial structures for MD. In analyzing the MD trajectories, we calculated the frequency with which carbon atoms at each of four groups of the tolterodine molecule approached to within a certain cutoff distance of the heme iron, and we also calculated binding free energy. We found that compared to the other three groups, the position to the experimentally determined SOM was close to the heme most frequently and had the lowest average ∆Gbinding. For selecting the MD initial structures, clustering on the basis of protein–ligand interaction fingerprints (PLIF) was substantially more robust at predicting accessibility compared with clustering based on root-mean-square deviations. These findings demonstrate that our method is applicable for a flexible ligand and that PLIF clustering is a promising method for selecting structures for MD. We succeeded to predict the experimentally determined SOM of tolterodine together with the appropriate binding mode. The predicted binding mode is useful to design metabolically more stable compounds.
