Furthermore, it is possible to further enhance the sampling of the REMD-EVB simulations by changing the number of exchanges and replicas. Thus, while the REMD-EVB simulations do not necessarily provide a significant advantage for shorter simulation timescales, they make substantially longer simulation timescales per trajectory computationally tractable in terms of human time, which allows for the capturing of, for instance, more significant conformational fluctuations along the chemical reaction coordinate that is possible using conventional EVB or higher precision QM/MM approaches. Finally, our REMD-EVB approach provides improved sampling of conformational space compared to a conventional EVB simulation of the same length. In order to evaluate the convergence of the different REMD-EVB protocols tested herein we turned to the statistical method of bootstrapping with replacement. Bootstrapping with replacement is a resampling procedure that generates many resamples of a given dataset of observations (in our case the individual ΔG ‡ and ΔG0 values), with it being possible to have multiple repeats of a single observation in a bootstrap sample. For each sampling protocol and system, we generated 5000 bootstrap resamples of the ΔG ‡ and ΔG0 values (separately) and used this to calculate new ΔG ‡ and ΔG0 average values. These averages were used to calculate probability densities for the calculated activation (ΔG ‡ , Figure ) and reaction (ΔG0, Figure ) free energies for each REMD-EVB simulation set. Note that the 10 x 100 run for yTPI is an outlier in this analysis, due to loop 6 opening in some of the replicas (a problem we have discussed at length in prior work ). From this data, it can be seen that the energy distributions fall in a narrow and overlapping range irrespective of simulation length. However, increasing the sampling time affects the distribution of the reaction free energies (Figure ), and also the calculated activation free energies in the case of PTP1B, and therefore the longer sampling times made accessible by Q-RepEx are beneficial. In order to quantify the bootstrapping results, we determined the 95% confidence intervals (CIs) which are provided in full in Tables and. By calculating the range between the lower and upper bounds of the CIs obtained for each system and sampling strategy, we identified the 10