As with our prior model systems, we utilized both the ML and statistical analysis modules available in our package to generate five models for both the R1 and R4 variants (three models with the ML module, and two with the statistical analysis module). We quantified the error in our regression ML models by calculating the mean absolute error (MAE) and root mean squared error (RMSE) on a validation set containing 15% of the data (Table ). For all six ML models generated (three for each of the R1 and R4 variants), the MAEs and RMSEs were substantially smaller than the differences in W50 χ2 dihedral angle values separating the "A" and "C" conformational states (Table ), suggesting the models produced for both KE07 variants can easily discriminate between the two states. As our goal was to compare the differences in per feature and per residue scores obtained for two different enzyme variants (i.e., the R1 vs. R4 variants), highlighting only some of the important features/residues instead of all (as we observed occur for our ML results in both PTP1B and PDZ3) would give rise to misleading comparisons. We therefore focused our analysis on the results obtained from the statistical analysis package, in particular those generated using the mutual information (MI) metric (as both metrics gave very similar per residue scores, with R 2 values of ~0.8, see