39 US windows were generated, ranging from 1 to 20 Å with a width of 0.5 Å between them. CV was chosen as the distance between the center of mass formed by the interfacial residues isolated by Bayas et al. on CD2 and CD58 (Table in ref ). A spring constant of 10 kcal/mol.Å 2 was employed to restrain the system along the chosen CV. Each window was run for 5 ns for equilibration and then for 50 ns. Histogram overlap as well as the PMF curve as a function of the simulation time allocated per window were employed to check the convergence of the simulations (SI Figure ). The final US PMF show a slow decrease of the free energy barrier with the simulation time, suggesting a slow convergence to about 12.5 kcal/mol. Binding affinity was found to be experimentally around 7.1 ± 0.03 kcal/mol, suggesting that our simulations are not converged. In order to improve sampling within each window a new US was performed similar to the previous US protocol but now with an additional GaMD-dualwat potential applied in each window. The GaMD parametrization protocol and reweighting procedure are described in the Technical Appendix and in SI (Figure and Table ). The optimized GaMD-dualwat parameters σ 0 are equal to 1 and 3 kcal/mol for respectively dihedral and dual water modes. To further push the sampling, we coupled together GaMD, AS and US (ASUS-GaMD). We provide two reweighting schemes that either use modified Multistate Bennett Acceptance Ratio (MBAR) equations or the Rao-Blackwell estimator. The mathematical expressions are general and can be used with any weighted dynamics. Starting from an initial US simulation (≃ few ns), each window is decomposed in several AS independent trajectories with an additional GaMD-dualwat potential boost (GaMD). Here, we ran 2 iterations of 5×5 ns GaMD-US per window. The PMF evolution can be found in SI (Figure ) while the resulting PMF is depicted on Figure . We observe that ASUS-GaMD reach GaMD-US in one iteration, showing the sampling acceleration impact provided by the AS part within ASUS-GaMD. Note that the rough aspect of the PMF obtained with the methods involving a GaMD bias comes from the debiaising of the boost potential, as can be seen in previous work involving US and GaMD. Although a careful reweighting is needed for the different AS, GaMD and US layers, the overall ASUS-GaMD approach inherits the strong adaptive sampling advantages of being pleasantly parallelizable and considerably accelerates the PMF convergence.