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Rational Engineering of Enzyme Allosteric Regulation through Sequence Evolution Analysis

Overview of attention for article published in PLoS Computational Biology, July 2012
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Title
Rational Engineering of Enzyme Allosteric Regulation through Sequence Evolution Analysis
Published in
PLoS Computational Biology, July 2012
DOI 10.1371/journal.pcbi.1002612
Pubmed ID
Authors

Jae-Seong Yang, Sang Woo Seo, Sungho Jang, Gyoo Yeol Jung, Sanguk Kim

Abstract

Control of enzyme allosteric regulation is required to drive metabolic flux toward desired levels. Although the three-dimensional (3D) structures of many enzyme-ligand complexes are available, it is still difficult to rationally engineer an allosterically regulatable enzyme without decreasing its catalytic activity. Here, we describe an effective strategy to deregulate the allosteric inhibition of enzymes based on the molecular evolution and physicochemical characteristics of allosteric ligand-binding sites. We found that allosteric sites are evolutionarily variable and comprised of more hydrophobic residues than catalytic sites. We applied our findings to design mutations in selected target residues that deregulate the allosteric activity of fructose-1,6-bisphosphatase (FBPase). Specifically, charged amino acids at less conserved positions were substituted with hydrophobic or neutral amino acids with similar sizes. The engineered proteins successfully diminished the allosteric inhibition of E. coli FBPase without affecting its catalytic efficiency. We expect that our method will aid the rational design of enzyme allosteric regulation strategies and facilitate the control of metabolic flux.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 169 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Korea, Republic of 2 1%
United Kingdom 1 <1%
Canada 1 <1%
Chile 1 <1%
Russia 1 <1%
Denmark 1 <1%
Japan 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 157 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 31%
Researcher 25 15%
Student > Master 22 13%
Student > Bachelor 17 10%
Professor > Associate Professor 11 7%
Other 21 12%
Unknown 21 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 38%
Biochemistry, Genetics and Molecular Biology 35 21%
Chemistry 19 11%
Chemical Engineering 6 4%
Engineering 6 4%
Other 14 8%
Unknown 24 14%