The huge advantage of PCVA is its much lower computational cost compared to the CSA or FSA approach (see Supporting Information, Tab. S5). Our PCVA-based approach requires only a geometry optimization of the target system with a minimal QM region including substrates, which is followed by single-point calculations for the point-charge variation of each amino acid. In contrast, CSA is a very expensive approach based on large QM regions with up to 1000 atoms. For these large systems, geometry optimizations have to be performed for the holo and apo enzyme structure for several snapshots along the reaction coordinate. FSA, even though being much cheaper than CSA, still needs as many geometry optimization as there are amino acids in the system for the minimal QM region plus one additional residue in each calculation. A rather similar approach to our PCVA-based construction of the QM region, the charge deletion analysis (CDA), is mostly reported for the usage with mediumsized QM regions, which also increases the required computational effort. Of course, our PCVA-based approach can also be applied for larger QM regions, but we found that this does not improve the results substantially.