To look for large-scale, slow protein motions that might account for slow convergence of the mean energy, we applied principal component analysis (PCA) to the simulated trajectories of the complexes and free ligands. PCA is commonly used to determine the essential dynamics of a simulation by reducing the dimensionality of the trajectory motions. Principle components, or PCs, are the eigenvectors obtained from diagonalizing the covariance matrix of a trajectory, and the eigenvector with the largest eigenvalue is the linear combination of Cartesian coordinates that captures the most variance. We used the cpptraj program to obtain the first three PCs for concatenated trajectories of the simulation replicates for each system (Run A and Run B) and then to project the individual trajectories onto each PC. All atoms of the ligands, but only the Cα atoms of the proteins, were included in these analyses.