Abstract
The cupin-type phosphoglucose isomerase (PfPGI) from the hyperthermophilic archaeon Pyrococcus furiosus catalyzes the reversible isomerization of glucose-6-phosphate to fructose-6-phosphate. We investigated PfPGI using protein-engineering bioinformatics tools to select functionally-important residues based on correlated mutation analyses. A pair of amino acids in the periphery of PfPGI was found to be the dominant co-evolving mutation. The position of these selected residues was found to be non-obvious to conventional protein engineering methods. We designed a small smart library of variants by substituting the co-evolved pair and screened their biochemical activity, which revealed their functional relevance. Four mutants were further selected from the library for purification, measurement of their specific activity, crystal structure determination, and metal cofactor coordination analysis. Though the mutant structures and metal cofactor coordination were strikingly similar, variations in their activity correlated with their fine-tuned dynamics and solvent access regulation. Alternative, small smart libraries for enzyme optimization are suggested by our approach, which is able to identify non-obvious yet beneficial mutations.
Highlights
An enormous variety of enzymes can be found in nature, providing a rich source of potential biocatalysts for industrial purposes
We describe our protein engineering investigations on the hyperthermophilic Pro132 and type cupin-type furiosus phosphoglucose isomerase (PfPGI)
The PfPGI mutant library was created by BaseClear (The Netherlands), and the genes were cloned in expression vector pET24d (Novagen)
Summary
An enormous variety of enzymes can be found in nature, providing a rich source of potential biocatalysts for industrial purposes. In the course of natural evolution, these enzymes have been optimized to function optimally in in vivo environments, which may differ substantially from in vitro industrial conditions. Often there is a need for protein engineering to optimize proteins for their applicability in an industrial setting. This is achieved by the generation of large libraries of protein variants, from which mutants with improved features are selected. Smart library design requires the identification of key residues, often through functional predictions using bioinformatics followed by high-throughput experimental analyses
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