To amend this outcome, we utilized PCA within PyCPET, to decompose variations in 𝐸𝐸 "⃗ (𝑟𝑟) between WT and the mutant LADH, including magnitudes and geometry. Among the PCA components, we observed only two 𝐸𝐸 "⃗ (𝑟𝑟) components that clearly distinguished the mutants (Fig. ). PC0 is an 𝐸𝐸 "⃗ (𝑟𝑟) pointing generally in the direction opposite to the C=O bond dipole (Fig. , left). The WT LADH, projected onto PC0, fluctuates around 0, having no alignment to it, while the S48T mutant is bimodal with a slight positive bias. Along PC0, these two variants have no clear separation in their mean projection, and the component points to residue 48 -the bimodal nature of S48T is likely due to varying conformations of the residue's hydroxo-group, not present in WT (Extended Data Fig. ). On the other hand, PC1 shows that there is a dominant direction from the oxygen toward the carbon in the C=O bond (Fig. , right). S48T is negatively aligned with this field component, while the WT Co-LADH is positively aligned with this field. Therefore, S48T exerts a field throughout dynamics more strongly aligned with the C=O dipole compared to the WT, confirming the experimental result by Zheng, et. al . This brings up an important consideration with using PCA as an analytical tool -components with the most variability are not always most physically informative, chemical intuition required for interpretation, and users should consider a larger set of components in analysis to not miss important elements. Thus, PyCPET and its 3-D electric field analysis features can tease out intricacies in the electric field geometry and compare mutants along dynamic trajectories. Overall, we see PyCPET as a rich tool kit that allows us and other users multiple avenues for analysis of active site electric fields and their dynamics.