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 ΔGbind predictions
were compared with experimental references. Additionally, the robustness
of CG-FMD-based ΔGbind 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 ΔGbind 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.