Origins of catalysis by computationally designed retroaldolase enzymes

Abstract

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...

Small molecule probes to quantify the functional fraction of a specific protein in a cell with minimal folding equilibrium shifts

“…We next evaluated the ability of fluorescent probe 3 to distinguish functional RA from its nonfunctional soluble conformations, using a pH titration. Previous studies revealed that acidic conditions protonate the K210 lysine, thus reducing the concentration of functional RA (16). We observed that the k cat /K m values decreased on buffer acidification (Fig.…”

“…S1A), a common natural enzyme scaffold (15). A pK a -perturbed active site lysine residue [K210 (16), pK a of 6.6] (Fig. S1B) is used for catalysis (Fig.…”

Conformational dynamics and enzyme evolution

“…In the design process, several possible catalytic mechanisms were considered, and the necessary catalytic site was designed based on the proposed mechanistic requirements and ported into several protein scaffolds. The most active designs had k cat /k uncat values on the orders up to 10 5 [81]. In the case of RA95, further directed evolution of the original design increased the activity by another six orders of magnitude over 19 rounds of directed evolution, and with the mutations at 23 different amino acid positions (figure 5) [82,83].…”

Dissecting enzyme function with microfluidic-based deep mutational scanning

“…The molecular bases of these rate accelerations are often complex, using multiple steps, multiple catalytic mechanisms, and relying on numerous molecular interactions, in addition to those provided by the main catalytic groups. This complexity imposes a significant barrier to understanding how an enzyme's sequence impacts its function and, thus, on our ability to rationally design biocatalysts with new or enhanced functions (2)(3)(4).…”