These two observations are particularly interesting, as the interaction networks calculated by KIN are based on evolutionary information across Class A β-lactamases, which have been evolutionary optimized to support the antibiotic degradation activity of this scaffold, and not the de novo Kemp eliminase activity. Despite this, KIN provides an effective tool at filtering hotspots, and we posit that positions that have both high KIF scores and low KIN conservation scores will be effective targets for optimization. Taken together, we believe this would be a viable pathway to perform hotspot prediction and optimization fully computationally prior to experimental testing, leading to highly efficient enzymes. On this premise, we suggest a simplified computational workflow to use KIF to guide experimental or computational protein optimization: (1) Use KIF to identify key interaction networks and/or residues with importance scores above a user-defined threshold.