Convergence and Sampling in Determining Free Energy Landscapes for Membrane Protein Association

[Image: see text] Potential of mean force (PMF) calculations are used to characterize the free energy landscape of protein–lipid and protein–protein association within membranes. Coarse-grained simulations allow binding free energies to be determined with reasonable statistical error. This accuracy relies on defining a good collective variable to describe the binding and unbinding transitions, and upon criteria for assessing the convergence of the simulation toward representative equilibrium sampling. As examples, we calculate protein–lipid binding PMFs for ANT/cardiolipin and Kir2.2/PIP(2), using umbrella sampling on a distance coordinate. These highlight the importance of replica exchange between windows for convergence. The use of two independent sets of simulations, initiated from bound and unbound states, provide strong evidence for simulation convergence. For a model protein–protein interaction within a membrane, center-of-mass distance is shown to be a poor collective variable for describing transmembrane helix–helix dimerization. Instead, we employ an alternative intermolecular distance matrix RMS (D(RMS)) coordinate to obtain converged PMFs for the association of the glycophorin transmembrane domain. While the coarse-grained force field gives a reasonable K(d) for dimerization, the majority of the bound population is revealed to be in a near-native conformation. Thus, the combination of a refined reaction coordinate with improved sampling reveals previously unnoticed complexities of the dimerization free energy landscape. We propose the use of replica-exchange umbrella sampling starting from different initial conditions as a robust approach for calculation of the binding energies in membrane simulations.