Distant non-obvious mutations influence the activity of a hyperthermophilic Pyrococcus furiosus phosphoglucose isomerase

Journal Article (2019)
Author(s)

Kalyanasundaram Subramanian (Wageningen University & Research)

Karolina Mitusińska (Silesian University of Technology)

John Raedts (Wageningen University & Research)

Feras Almourfi (National Center of Genome Technology, King Abdulaziz City)

Henk Jan Joosten (Bio-Prodict, Nijmegen)

Sjon Hendriks (Wageningen University & Research)

Svetlana E. Sedelnikova (University of Sheffield)

Servé W.M. Kengen (Wageningen University & Research)

Wilfred R. Hagen (TU Delft - Applied Sciences)

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Research Group
BT/Biocatalysis
DOI related publication
https://doi.org/10.3390/biom9060212 Final published version
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Publication Year
2019
Language
English
Research Group
BT/Biocatalysis
Journal title
Biomolecules
Issue number
6
Volume number
9
Article number
212
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270
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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.