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Structural and Biochemical Insights into the Function and Evolution of Sulfoquinovosidases

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.


Safety statement
No unexpected or unusually high safety hazards were encountered in this work.
The resulting plasmid encoded a C-terminal His 6 tagged protein ( Supplementary Fig. 2).

Mutagenesis
Site directed mutagenesis of AtSQase and EcYihQ was accomplished using a PCR approach with the oligonucleotide primers listed in Supplementary Table 1. As an example, the EcYihQ-Q288E expression construct was assembled using two rounds of PCR. First, two amplification reactions were conducted using pET29-EcYihQ as template and oligonucleotide primer pairs: T7 / YihQ-Q288E-r and T7-Term / YihQ-Q288E-f. The products of these reactions were purified by agarose gel electrophoresis and mixed in equimolar amounts to serve as a template for a third amplification reaction using T7 and T7-term as primers. This PCR product was cloned into pET29b(+), the sequence verified by Sanger sequencing, and expressed and purified as for wildtype enzyme.

Protein expression and purification
All wild-type and mutant SQases were expressed in the 'NEB Express' Escherichia coli strain using 'S-broth' medium (25 g/L tryptone, 15 g/L yeast extract and 5 g/L of NaCl). Transformed The column was washed with 15 ml binding buffer and the protein was eluted using elution buffer (50 mM NaP i , 500 mM NaCl, 500 mM imidazole, pH 7.5). Fractions containing product (as determined by SDS-PAGE) were further purified by size exclusion chromatography on a HiLoad 16/600 Superdex 200 column using 50 mM NaP i , 150 mM NaCl, pH 7.5. Protein yield for all proteins was >10 mg per litre of culture.

H NMR analysis of SQGro hydrolysis by EcYihQ
EcYihQ-catalysed hydrolysis of SQGro was monitored by 1

Isothermal titration calorimetry
The affinities of IFGSQ for EcYihQ and AtSQase were measured by isothermal titration calorimetry (ITC). Titrations were performed with a MicroCal Auto-iTC200 system (GE Healthcare). Assays were conducted in duplicates at 25 ºC. 600 µM and 800 µM IFGSQ were titrated into the ITC cell containing 60 µM of purified AtSQase and EcYihQ , respectively.
Dissociation constants (KD) for each titration were calculated and averaged using MicroCal PEAQ-ITC analysis software.

Bacterial growth
Methyl α-D-sulfoquinoviside (SQMe) and D-sulfoquinovose (SQ) was purchased from MCAT  20% MPD and ethylene glycol were used as cryoprotectant for all the crystals before being flash frozen in liquid nitrogen. Diffraction data for all the crystals were collected at 100 K on beamline i04 or i04-1 the Diamond Light Source and were processed using the xia2 implementation of XDS and programs from CCP4 suite. 4 The structure was determined by molecular replacement using PDB 5AEDwith maximum likelihood refinement of the protein model using numerous cycles of REFMAC and manual correction using COOT. The statistics of the data processing and structure refinement are listed in Supplementary Information Table 1.

Sequence analysis
Sequences from the Glycoside Hydrolase Family 31 family (GH31) deposited on the Carbohydrate-Active enZymes Database (www.cazy.org) 5 were obtained and clustered using CD-HIT 6 (90% similarity) in order to compile a representative set. This was probed using blastp with the sequences from the E. coli and A. tumefacens SQases as queries and candidate hits were filtered based on evalue (< 1e -40 ).
A Multiple Sequence Alignment (MSA) was generated using MAFFT (default parameters) 7 and sequences were further selected based on matches of the two previously identified motifs: KERWY and QQRWY. This generated a final alignment of 84 sequences. A phylogenetic tree was generated based on the MSA with PhyML, 8 with initial tree generation done with BIONJ and the LG substitution model employed with branch support with bootstrap analyses (100). Tree diagram was generated with iTOL. 9 Co-evolution analysis was performed using the MISTIC platform, which uses Mutual Information (MI) to infer coevolution between residue pairs, 10 using a MI Z-score threshold of >6.5, which leads to a specificity of 95%. Residue pairings with strong coevolution signals near the binding pocket were assessed.

Structural bioinformatics
Structure guided approaches can provide valuable insight into the molecular mechanism of mutations. 11-16 mCSM 17 and DUET 18 are machine-learning methods that use graph-based structural signatures to represent the three dimensional residue environment structure in order to quantitatively predict the effects of missense mutations on protein stability, and binding affinities to their partners. The mCSM-Lig, 19 DynaMut 20 and SDM 21 scoring systems were used to predict the effects of mutations on ligand affinity and protein stability respectively, based on changes in the Gibbs free energy of folding, using the experimental crystal structures of EcYihQ and AtSQase.
Computational docking between experimental crystal structures of EcYihQ and AtSQase and 2'R-SQGro was performed with Autodock. 22