To assess the rate of convergence, Kullback-Leibler divergence (KLD) analysis was performed . KLD is a statistical analysis approach, which measures the difference of one probability distribution with another probability distribution serving as the reference. In this approach the overlap of the principal component (PC) projection histograms is used as a measure of convergence between two independent runs. Therefore, if these two runs experience the same motions and sample the same conformational space, the overlap of their PCs increases. A well converged simulation is the one in which PC projection histograms have a good overlap. In order verify the results of KLD analyses, we conducted another type of convergence analysis, where the populations of each cluster observed along the trajectory was calculated (Figure ). After performing cluster analyses on the T-REMD ensemble at 299.5 K, clusters observed over 1% were first determined. Observed percentages of these clusters along the T-REMD trajectory were then calculated at every time step covering 500 ns snapshots (Figure ). It was observed that the two major clusters, GsynGanti/GantiGsyn and GantiGsyn/GantiGsyn, were the dominant ones fluctuating around 40%, which were reasonably well with the predictions displayed in Figure .