Speaker
Description
Docking and molecular dynamics (MD) are widely used to predict drug–protein interactions. While docking provides rapid starting structures, MD can equilibrate ligand binding poses at the cost of greater computational demand. Automated pipelines remain limited, particularly for systems with covalently bound cofactors or metals, or for flexible protein docking.
We present an automated workflow built on the GPU-accelerated OpenMM engine for large-scale refinement of docked complexes. The pipeline integrates seamlessly with both rigid and flexible docking methods and automates ligand parameterization for the majority of cases. In benchmark tests, automatic setup succeeded for ~90% of ligands with rigid docking engines, while RoseTTAFold All-Atom achieved ~30% due to ligand conformation artifacts.
This workflow enables high-throughput, GPU-powered refinement of cytochrome P450 complexes, making MD refinement feasible for large numbers of docked complexes.
