B-P.22: Co-folding based sites-of-metabolism prediction of cytochrome P450 2C9
Authors
Keywords
co-folding, docking, cytochrome P450 2C9
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Accurate prediction of enzymatic reaction sites remains a central challenge in computational chemistry and metabolic modelling. Cytochromes P450 (CYPs), particularly the CYP2C9 isoform, are critical enzymes involved in the metabolism of approximately 20% of marketed drugs, xenobiotics, and endogenous compounds. Identifying precise ligand reaction centres is essential for predicting metabolic pathways and supporting rational drug design.
In this study, we evaluated the performance of recent co-folding frameworks, specifically Boltz 1, Boltz 2 and AlphaFold 3 in predicting ligand sites-of-metabolism. Co-folding allows for simultaneous prediction of protein and ligand conformations, accounting for induced-fit effects. A dataset of 176 unique reactants corresponding to 310 metabolic reactions catalysed by CYP2C9 was analyzed. Predicted ligand poses and their respective site-of-metabolism within ligand were evaluated based on their atomic spatial proximity to the heme cofactor and compared with experimental data from the DrugBank database.
Both approaches indicate that co-folding methods can capture key aspects of enzyme ligand interactions. Boltz models achieved a 49% per-reactant success in identifying correct reaction sites, slightly outperforming AlphaFold 3 (42%). To further contextualize these findings, the performance of co-folding is compared to traditional molecular docking. Using a single rigid CYP2C9 (5W0C) with Vina, docking achieved only a 23% success rate in the identification of the site-of-metabolism, underperforming co-folding on this dataset. The combination of methods has shown increased successes as each method performed well for slightly different set of ligands. However, 1/3 of the site-of-metabolism was not predicted by any method tested.While no single structural features consistently determine the prediction success, these results highlight co-folding as a promising approach for improving the accuracy of metabolic site prediction and enhancing computational drug development tools.
Co-authors: Karel Berka, Vaclav Bazgier
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