Structural and mechanistic analysis of a β-glycoside phosphorylase identified by screening a metagenomic library
Bibliographic record
Abstract
Glycoside phosphorylases have considerable potential as catalysts for the assembly of useful glycans for products ranging from functional foods and prebiotics to novel materials. However, the substrate diversity of currently identified phosphorylases is relatively small, limiting their practical applications. To address this limitation, we developed a high-throughput screening approach using the activated substrate 2,4-dinitrophenyl β-d-glucoside (DNPGlc) and inorganic phosphate for identifying glycoside phosphorylase activity and used it to screen a large insert metagenomic library. The initial screen, based on release of 2,4-dinitrophenyl from DNPGlc in the presence of phosphate, identified the gene bglP, encoding a retaining β-glycoside phosphorylase from the CAZy GH3 family. Kinetic and mechanistic analysis of the gene product, BglP, confirmed a double displacement ping-pong mechanism involving a covalent glycosyl–enzyme intermediate. X-ray crystallographic analysis provided insights into the phosphate-binding mode and identified a key glutamine residue in the active site important for substrate recognition. Substituting this glutamine for a serine swapped the substrate specificity from glucoside to N -acetylglucosaminide. In summary, we present a high-throughput screening approach for identifying β-glycoside phosphorylases, which was robust, simple to implement, and useful in identifying active clones within a metagenomics library. Implementation of this screen enabled discovery of a new glycoside phosphorylase class and has paved the way to devising simple ways in which enzyme specificity can be encoded and swapped, which has implications for biotechnological applications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".