We have investigated recently reported computationally designed retroaldolase enzymes with the goal of understanding the extent and the origins of their catalytic power. Direct comparison of the designed enzymes to primary amine catalysts in solution revealed a rate acceleration of 10 5 -fold for the most active of the designed retroaldolases. Through pH-rate studies of the designed retroaldolases and evaluation of a Brønsted correlation for a series of amine catalysts, we found that lysine pK a values are shifted by 3-4 units in the enzymes but that the catalytic contributions from the shifted pK a values are estimated to be modest, about 10-fold. For the most active of the reported enzymes, we evaluated the catalytic contribution of two other design components: a motif intended to stabilize a bound water molecule and hydrophobic substrate binding interactions. Mutational analysis suggested that the bound water motif does not contribute to the rate acceleration. Comparison of the rate acceleration of the designed substrate relative to a minimal substrate suggested that hydrophobic substrate binding interactions contribute around 10 3 -fold to the enzymatic rate acceleration. Altogether, these results suggest that substrate binding interactions and shifting the pK a of the catalytic lysine can account for much of the enzyme's rate acceleration. Additional observations suggest that these interactions are limited in the specificity of placement of substrate and active site catalytic groups. Thus, future design efforts may benefit from a focus on achieving precision in binding interactions and placement of catalytic groups.amine | Brønsted correlation | catalytic mechanism | computational enzyme design | retroaldol reaction B ecause natural enzymes catalyze reactions with tremendous rate accelerations and specificities, a long-standing goal in enzymology and protein engineering has been to reliably design new enzyme catalysts for chemical reactions of interest. Computational protein design offers a promising tool for achieving this goal. Computational protein design methods use specialized potential energy functions in combination with search algorithms to optimize an amino acid sequence for a given protein structure and function (1-3). These methods have been adapted to the challenge of designing enzyme active sites (4-8).Although new enzymatic activities have been designed computationally, the resulting catalysts have catalytic efficiencies (k cat ∕K M values) of about 1-100 M −1 s −1 (4,7,8), considerably less than values of 10 5 -10 9 M −1 s −1 typical of natural enzymes, and similar to those of early catalytic antibodies. In contrast to catalytic antibody methods and other stochastic processes, however, the potential for success of computational enzyme design is tied to the predictive power of the computational model. Thus, future improvement of our ability to computationally design enzymatic activity will require ongoing rigorous assessment of the successes and failures of the design process.We therefore soug...