Efficient Protein-Ligand Binding Free Energy Estimation with Coarse-Grained Funnel Metadynamics

Despite considerable advances in computational chemistry, bridging the gap between the accuracy of all-atom molecular dynamics (AA-MD) and the high-throughput capabilities of docking remains an unsolved problem in protein-ligand binding free energy predictions. In this work, we propose to address this challenge through coarse-grained funnel metadynamics (CG-FMD) with the Martini 3 force field. This approach combines the reduced computational cost of a CG representation with state-of-the-art enhanced sampling techniques and the interpretability of a physics-based force field. Specifically, the binding of colchicine to two different protein targets was modeled at both AA and CG resolutions, and the corresponding ΔG_bind predictions were compared with experimental references. Additionally, the robustness of CG-FMD-based ΔG_bind predictions was evaluated with respect to various aspects of the simulation setup by collecting more than 7 ms of CG-FMD simulations. The optimal simulation protocol has been further validated against a limited set of compounds chemically different from colchicine. The results demonstrate that CG-FMD yields ΔG_bind estimates comparable to experimental values while requiring only a fraction of the computational cost of AA-MD simulations. Moreover, the extensive sampling achievable with CG-FMD reduces statistical uncertainty in the final predictions, effectively compensating for the simplified system representation. Future work should build upon these methodological insights to broaden the scope of ligands and targets explored.