5OHT image
Entry Detail
PDB ID:
5OHT
Keywords:
Title:
A GH31 family sulfoquinovosidase from E. coli in complex with aza-sugar inhibitor IFGSQ
Biological Source:
PDB Version:
Deposition Date:
2017-07-18
Release Date:
2018-08-08
Method Details:
Experimental Method:
Resolution:
1.87 Å
R-Value Free:
0.20
R-Value Work:
0.16
R-Value Observed:
0.16
Space Group:
P 1
Macromolecular Entities
Polymer Type:polypeptide(L)
Description:Sulfoquinovosidase
Chain IDs:A, B
Chain Length:686
Number of Molecules:2
Biological Source:Escherichia coli (strain K12)
Primary Citation
Structural and Biochemical Insights into the Function and Evolution of Sulfoquinovosidases.
ACS Cent Sci 4 1266 1273 (2018)
PMID: 30276262 DOI: 10.1021/acscentsci.8b00453

Abstact

An estimated 10 billion tonnes of sulfoquinovose (SQ) are produced and degraded each year. Prokaryotic sulfoglycolytic pathways catabolize sulfoquinovose (SQ) liberated from plant sulfolipid, or its delipidated form α-d-sulfoquinovosyl glycerol (SQGro), through the action of a sulfoquinovosidase (SQase), but little is known about the capacity of SQ glycosides to support growth. Structural studies of the first reported SQase (Escherichia coli YihQ) have identified three conserved residues that are essential for substrate recognition, but crossover mutations exploring active-site residues of predicted SQases from other organisms have yielded inactive mutants casting doubt on bioinformatic functional assignment. Here, we show that SQGro can support the growth of E. coli on par with d-glucose, and that the E. coli SQase prefers the naturally occurring diastereomer of SQGro. A predicted, but divergent, SQase from Agrobacterium tumefaciens proved to have highly specific activity toward SQ glycosides, and structural, mutagenic, and bioinformatic analyses revealed the molecular coevolution of catalytically important amino acid pairs directly involved in substrate recognition, as well as structurally important pairs distal to the active site. Understanding the defining features of SQases empowers bioinformatic approaches for mapping sulfur metabolism in diverse microbial communities and sheds light on this poorly understood arm of the biosulfur cycle.

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