Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Abstract

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

Keywords: designer enzyme; directed evolution; enzyme architecture; enzyme design; natural evolution.

Fig. 1.

The maximum entropy model for natural sequences underlies the laboratory evolution of Kemp eliminase. (A) Scheme of the Kemp elimination with 5-nitrobenzisoxazole as substrate. The elimination reaction involves a catalytic base that deprotonates the carbon. In the KE07 series, the active site contains a glutamic acid residue as the catalytic base and a lysine residue to stabilize the negative charge of the oxygen. (B and C) The connection between the MaxEnt model for naturally evolved sequences and laboratory evolution of the abiological Kemp eliminase. The schematic pictures of the landscape from the model are plotted for laboratory evolution for the KE07 series. ${\text{ΔΔG}}^{\ne }$ is the reaction barrier for different Kemp eliminases. PDB entry used in rendering the structure is 2RKX with substrate docked. (B) Directed evolution: (Left) Residues that are mutated in the KE07 series of directed evolution are highlighted in cyan. (Right) The $E_{\text{MaxEnt}}$ increases as a result of sacrificing stability in directed evolution; the catalytic power is enhanced due to the stability-activity trade-off. (C) Catalytic-active remote regions: (Left) Residues with experimentally generated single-mutation data are highlighted in marine. (Right) The $E_{\text{MaxEnt}}$ anticorrelates with catalytic power for such regions, and optimizing the statistical energy could improve the activity of Kemp elimination.

Fig. 2.

The maximum entropy model for natural sequences is correlated with the catalytic power of designs in directed evolution. Strong positive correlations between $E_{\text{MaxEnt}}$ and $\log k_{\text{cat}}/K_{\text{M}}$ (A) and $\log k_{\text{cat}}$ (B) for the KE07-directed evolution are shown with the least-squares regression lines included. Each dot represents a design with a larger dot size indicating later rounds in directed evolution. We shifted $E_{\text{MaxEnt}}$ by a constant so that the initial design has a zero value.

Fig. 3.

Enzyme catalytic power and stability trade-off in directed evolution of designer enzyme. (A and B) Strong negative correlations between protein stability quantified by melting temperature ($T_{\text{m}}$) and enzyme catalytic power expressed by $\log k_{\text{cat}}/K_{\text{M}}$ (A) and $\log k_{\text{cat}}$ (B) for the KE07-directed evolution. (C) A strong negative correlation between $T_{\text{m}}$ and $E_{\text{MaxEnt}}$. The $E_{\text{MaxEnt}}$ is the same as that in Fig. 2 for the same mutant.

Fig. 4.

The maximum entropy model for natural sequences is anticorrelated with the catalytic power of mutants in catalytic-active remote regions. Strong negative correlations between $E_{\text{MaxEnt}}$ and $\log k_{\text{cat}}/K_{\text{M}}$ (A) and $\log k_{\text{cat}}$ (B) for the catalytic-active remote region are shown with the least-squares regression lines included. Each dot represents one single mutation. We shifted $E_{\text{MaxEnt}}$ by a constant so that the best-evolved design has a zero value.