However, as we demonstrate in this work the process can be further optimized, by avoiding the preparation of random (saturation or combinatorial) libraries at the determined hotspots (as would be the standard practice). Rather, following recent work, we use FuncLib, a novel computational methodology that combines Rosetta design with phylogenetic analysis, to rank enzyme variants with multiple mutations according to predicted stability. This approach has been successfully used in a wide range of protein design applications. Specifically, we use here predictions from the FuncLib webserver to target sets of NMR-determined catalysis hotspots in such a way that the predicted variants may be expected to modulate enzyme activity without impairing stability. The use of FuncLib bypasses another major factor that contributes to making non-guided directed evolution inefficient, namely that since most mutations in a protein are destabilizing or even disruptive, screening of a random library with a significant mutational load involves the probing of variants with impaired stability which, in many cases, may not even fold properly. As a protein system to test our combined approach, we have selected a de novo enzyme capable of catalysing Kemp elimination through a classical proton-abstraction mechanism. Kemp elimination has been widely used as benchmark for de novo enzyme engineering. These studies obtained (at times substantial) improvement in Kemp eliminase activity over catalytically modest starting points. In contrast, in this work, the specific Kemp eliminase we are targeting here for further engineering is already substantially optimized in terms of both, stability and catalysis. That is, as a result of previous engineering efforts, it displays high catalysis levels, approaching in fact those of the best proton-abstraction Kemp eliminase to date which was the outcome of 17 rounds of directed evolution. Furthermore, it is a stable protein with a high denaturation temperature of 80 ºC, reflecting in part the fact that the original scaffold for the engineering was a resurrected ancestral lactamase with high stability. Based on these facts, overall, further optimization of our target Kemp eliminase appears a priori difficult. Yet, we find that a rather limited screening of just 25 variants predicted by our combined NMR/computational approach leads to a variant with ~3-fold improved activity (from an already very high starting point), with activities of the variants spanning a ~50-fold range, and without impairing protein stability. In fact, most of the predicted variants display substantially enhanced denaturation temperatures and purification yields. Moreover, we reach a catalytic efficiency ~4•10 5 M -1 s -1 with catalytic rate constant well above 1000 s -1 , i.e. above most reported values for engineered de novo enzymes and modern natural enzymes, making our Kemp eliminase the most efficient engineered Kemp eliminase catalysing this reaction through proton abstraction. Finally, molecular dynamics and empirical valence bond simulations of the optimized variants, compared to the "wild-type" GNCA4-W229/F290W and computationally optimized GNCA4-12 variants indicate the importance of conformational plasticity in creating a pre-organized active site for efficient catalysis, both through conformational enrichment of catalytically productive conformations of the active site tryptophan, W290, which is in turn important for substrate stabilization in the active site, as well as through elimination of non-reactive substrate binding conformations. This mirrors observations from de novo designed Kemp eliminases but on an enzyme with a de novo active site generated using a minimalist design involving a single mutation, that may mimic the actual emergence of new enzymes during natural evolution.