Abstract
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.
Highlights
Enzymes are green catalysts with unmatched catalytic pro ciencies,[1] and with widespread applications in biotechnology as extracellular catalysts for a host ofchemical processes, from organic synthesis to developing new pharmaceuticals, biofuels, or bioremediation agents, to name but a few examples
We recently demonstrated that a simple hydrophobic-to-ionizable residue substitution (Fig. 1) is sufficient to generate a de novo active site capable of highly pro cient Kemp eliminase activity for the cleavage of 5-nitrobenzisoxazole in Precambrian b-lactamases obtained by ancestral inference,[35] with the best of our designs showing catalytic pro ciencies only two orders of magnitude lower than the best designed Kemp eliminase obtained through iterative design followed by 17 rounds of directed evolution.[38]
De novo generation of enzyme active sites for Kemp elimination has proved amenable to rational design.[15,35,38,44,46,94,101]
Summary
Recent years have seen an explosion of interest in computational enzyme design,[11,12,13,14] propelled in large part by early successes in de novo enzyme design through gra ing 6134 | Chem. Experimental validation of the twenty best scoring FuncLib predictions through biochemical and structural analysis allows us to identify 4 variants with signi cantly enhanced catalytic efficiency and improved turnover number, the best of which reach catalysis levels (kcat/KM of $2 Â 104 MÀ1 sÀ1 and kcat of $102 sÀ1) for the cleavage of 5-nitrobenzisoxazole that compare favourably with that of naturally occurring enzymes.[19] In addition, we demonstrate that the empirical valence bond (EVB) approach[54] can reproduce the experimental free energy barriers for the optimized eliminases to within $2 kcal molÀ1, raising the possibility of further enhancing the stability-guidance of FuncLib on the basis of EVB-based computational predictions of catalytic activity. We demonstrate a simple computational protocol with tremendous potential for biocatalysis
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