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Department of Biochemistry, Duke University Medical Center, Durham, NC 27710, USA.
We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top-ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for computational enzyme redesign that are experimentally shown to result in significant additional improvements in the target substrate specificity. The mutant with the highest activity for a noncognate substrate exhibits 1/6 of the wild-type enzyme/wild-type substrate activity, further confirming the feasibility of our computational approach. Our results suggest that structure-based protein design can identify active mutants different from those selected by evolution.
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