Automated Structural Refinement of Docked Complexes in Cytochrome P450 Using Molecular Dynamics

Molecular docking is a standard tool for predicting ligand binding, yet refining these poses via molecular dynamics (MD) remains challenging to automate for metalloproteins like Cytochrome P450 (CYP450). Here, we introduce DOCKTOPUS (Dock to P450) and CYCLOPS (CYtochrome Complex Ligand Optimization with Protein Simulation) Python packages designed to automate the docking and molecular dynamics setup for CYP450-ligand complexes. These tools integrate seamlessly with representative predictive engines AutoDock Vina, GNINA, GalaxyDock2-HEME, and RoseTTAFold All-Atom (RFAA) by automatically handling the complex parameterisation of the covalently bound heme-thiolate cofactor and ligand topologies, while also providing capabilities to filter out unphysical ligand geometries generated by RFAA. We validated the pipeline through a medium-sized case study to enable a rigorous statistical analysis. In crossdocking scenarios RFAA achieved a mean geometric drift relative to experiments approximately three times lower than that of AutoDock Vina. Our structural and pharmacophoric analyses revealed that subsequent MD simulations provide no statistically significant geometric refinement towards the experimental crystal structure. This behaviour was remarkably consistent: we observed no significant differences by specific interaction type or ligand flexibility. These findings suggest that short MD refinement with contemporary classical force fields for organometallic systems might have limited effects beyond pose validity. Furthermore the DOCKTOPUS and CYCLOPS packages offer a robust platform for statistical testing of docking and molecular dynamics effects.