6ty6

Publication Abstract from PubMed

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 ( approximately 2 x 10(4) M(-1) s(-1) and approximately 10(2) 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 approximately 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.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening.,Risso VA, Romero-Rivera A, Gutierrez-Rus LI, Ortega-Munoz M, Santoyo-Gonzalez F, Gavira JA, Sanchez-Ruiz JM, Kamerlin SCL Chem Sci. 2020 May 19;11(24):6134-6148. doi: 10.1039/d0sc01935f. eCollection 2020, Jun 28. PMID:32832059^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.