WCLC 2025 - Posters & ePosters-P3.03.24 Oxydock: Optimizing Lung Cancer-Targeted Therapy Through Oxygen-Responsive Molecular Docking

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The study presents OxyDock, a novel molecular docking simulator specifically designed to optimize lung cancer-targeted therapies by accounting for hypoxic tumor microenvironments. Hypoxia (oxygen levels ≤2%) is common in lung tumors and induces post-translational modifications (PTMs) such as phosphorylation, acetylation, and methylation. These PTMs alter protein structures and ligand-binding dynamics, reducing the effectiveness of traditional targeted therapies and complicating cancer treatment.<br /><br />Traditional docking platforms often assume static protein conformations and fail to incorporate hypoxia-induced structural changes and altered biochemical conditions, leading to inaccurate drug-receptor interaction predictions. To address this, the authors developed OxyDock, which integrates hypoxia-responsive PTMs into protein structures, enabling docking simulations that more closely mimic in vivo tumor conditions. OxyDock employs AutoDock Vina for docking combined with an XGBoost machine learning model, and it features a user-friendly graphical interface with direct visualization of docking results under variable oxygen parameters.<br /><br />Protein structures were sourced from PDB and PubChem, and ligands were docked to hypoxia-modified proteins. The platform's predictive accuracy was benchmarked against experimental binding affinity data and other docking tools, using lung cancer cell line data (A549) under both normoxic and hypoxic conditions. OxyDock demonstrated superior performance with a root mean square error (RMSE) of 1.16 in binding affinity prediction, notably capturing atypical interactions ("outliers") better than competing platforms.<br /><br />In conclusion, OxyDock effectively models oxygen-dependent conformational changes, providing a robust, experimentally-validated tool for simulating targeted drug efficacy in hypoxic lung cancer environments. This capability makes OxyDock a valuable in silico resource for optimizing cancer therapeutics by reflecting realistic tumor biology not accounted for in existing docking frameworks.

Asset Subtitle

Arnav Pemmaraju

Meta Tag

Speaker Arnav Pemmaraju

Topic Tumor Biology – Translational Biology

Keywords

OxyDock

molecular docking simulator

lung cancer

hypoxic tumor microenvironment

post-translational modifications

protein phosphorylation

protein acetylation

protein methylation

AutoDock Vina

XGBoost machine learning