Computational design and characterisation of artificial enzymes for Kemp elimination

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Computational strategies for the design of new enzymatic functions

In this contribution, recent developments in the design of biocatalysts are reviewed with particular emphasis in the de novo strategy. Studies based on three different reactions, Kemp elimination, Diels-Alder and Retro-Aldolase, are used to illustrate different success achieved during the last years. Finally, a section is devoted to the particular case of designed metalloenzymes. As a general conclusion, the interplay between new and more sophisticated engineering protocols and computational methods, based on molecular dynamics simulations with Quantum Mechanics/Molecular Mechanics potentials and fully flexible models, seems to constitute the bed rock for present and future successful design strategies.

A computational strategy for altering an enzyme in its cofactor preference to NAD(H) and/or NADP(H)

Coenzyme engineering, especially for altered coenzyme specificity, has been a research hotspot for more than a decade. In the present study, a novel computational strategy that enhances the hydrogen-bond interaction between an enzyme and a coenzyme was developed and utilized to alter the coenzyme preference. This novel computational strategy only required the structure of the target enzyme. No other homologous enzymes were needed to achieve alteration in the coenzyme preference of a certain enzyme. Using our novel strategy, Gox2181 was reconstructed from exhibiting complete NADPH preference to exhibiting dual cofactor specificity for NADH and NADPH. Structure-guided Gox2181 mutants were designed in silico and molecular dynamics simulations were performed to evaluate the strength of hydrogen-bond interactions between the enzyme and the coenzyme NADPH. Three Gox2181 mutants displaying high structure stability and structural compatibility to NADH/NADPH were chosen for experimental confirmation. Among the three Gox2181 mutants, Gox2181-Q20R&D43S showed the highest enzymatic activity by utilizing NADPH as its coenzyme, which was even better than the wild-type enzyme. In addition, isothermal titration calorimetry analysis further verified that Gox2181-Q20R&D43S was able to interact with NADPH but the wild-type enzyme could not. This novel computational strategy represents an insightful approach for altering the cofactor preference of target enzymes.