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• W1987862016 abstract "Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound’s potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity–accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity–accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to Km values leads to an improvement in SOM prediction for substrates with low Km values (57% at top-1). While the current predictive power of the reactivity–accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein–ligand interactions and guide structural modifications of the ligand to increase its metabolic stability." @default.
• W1987862016 created "2016-06-24" @default.
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• W1987862016 date "2011-09-01" @default.
• W1987862016 modified "2023-10-18" @default.
• W1987862016 title "Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures" @default.
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• W1987862016 doi "https://doi.org/10.1016/j.ejmech.2011.05.067" @default.
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