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Enhancing a De Novo Enzyme Activity by Computationally-Focused Ultra-Low-Throughput Screening

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.

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Supplementary files

Article information


Submitted
05 Apr 2020
Accepted
18 May 2020
First published
19 May 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020, Accepted Manuscript
Article type
Edge Article

Enhancing a De Novo Enzyme Activity by Computationally-Focused Ultra-Low-Throughput Screening

V. Risso, A. Romero-Rivera, L. I. Gutierrez-Rus, M. Ortega-Muñoz, F. Santoyo-Gonzalez, J. A. Gavira, J. M. Sanchez-Ruiz and S. C. L. Kamerlin, Chem. Sci., 2020, Accepted Manuscript , DOI: 10.1039/D0SC01935F

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