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<p>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.
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Web-based tools for computational enzyme design
