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

preprint
revised on 04.04.2020 and posted on 06.04.2020 by Valeria A. Risso, Adrian Romero-Rivera, Luis I. Gutierrez-Rus, Mariano Ortega-Muñoz, Francisco Santoyo-Gonzalez, José A. Gavira, Jose Manuel Sanchez Ruiz, Shina Caroline Lynn Kamerlin

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. 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 a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point 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) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, 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 improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Funding

Knut and Alice Wallenberg Foundation

Human Frontier Science Program

Natural Sciences and Engineering Research Council

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FEDER-funds/Spanish Ministry of Economy and Competitiveness

History

Email Address of Submitting Author

lynn.kamerlin@kemi.uu.se

Institution

Uppsala University

Country

Sweden

ORCID For Submitting Author

0000-0002-3190-1173

Declaration of Conflict of Interest

No Conflict of Interest

Version Notes

Revised version 2.0.

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