Issue 24, 2020

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.

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

Supplementary files

Article information

Article type
Edge Article
Submitted
05 avr. 2020
Accepted
18 mai 2020
First published
19 mai 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 6134-6148

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

V. A. 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, 11, 6134 DOI: 10.1039/D0SC01935F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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