There are several avenues that could be explored to further develop our approach. First, our method does not account for the folding free energy of isolated monomers into helices prior to their assembly. This could be addressed via enhanced sampling MD protocols to compute folding free energies, 119-121 or alternatively, predicted through machine learning approaches. The simulation speeds for the current implicit solvent protocol range from 1350 ns/day for a dimer separation step to 670 ns/day for a hexamer separation step in the presence of all restraints on a NVIDIA RTX3080 GPU. All PMF calculations were run for uniform amounts of time to ensure convergence of the angle and dihedral equilibrium values and the free energy profiles, however it may be enough to run them for shorter timescales, which could significantly reduce simulation costs. While our approach can be used in explicit solvent simulations, the increased computational cost to achieve converged binding free energies may limit its scalability for large-scale applications. An interesting direction would be to derive transferable implicit solvent models using machine learning, trained on explicit solvent trajectories. We have also limited our pH studies to low and high pH conditions with fixed protonation states. A more realistic treatment of pH effects could be achieved by coupling our approach with constant-pH MD methods 125-127 to describe coiled-coil assemblies at pH values where a mixture of distinct protonation states is expected to coexist. Finally, our methodology could be applied to other classes of proteinprotein interactions by modifying the protocols used to apply helical or RMSD restraints.