We note here that KIF by design predicts important residues and interactions across the entire protein scaffold, and not just at known mutational hotspots. While any of these are potentially targets for design effort, it would be helpful to have a means to filter hotspots to subject to further optimization and not have to test the full KIF-predicted set. In this context, in prior work, we computed detailed residue interaction networks to calculate evolutionary conservation of such interactions in Class A β-lactamases. To test the efficacy of KIN as a tool for filtering hotspots, we have compared our KIF calculated hotspots against evolutionarily conserved positions predicted using KIN (Figure ). This analysis provides two important observations: (1) with two exceptions, interactions involving the ten hotspot residues that are part of the NMR-determined βset also have low KIN evolutionary conservation scores, indicating that they would be good targets for design, as evidenced by our successful optimization of these positions. Further, (2) a number of NMR-determined α-set residues have non-negligible KIN scores, indicating that these positions are less likely to be amenable to successful optimization, again consistent with our unsuccessful attempts to improve activity when targeting these positions.