Engineering Ribonucleoside Triphosphate Specificity in a Thymidylyltransferase
Bibliographic record
Abstract
Nature's glycosylation catalysts, glycosyltransferases, indirectly manipulate and control many important biological processes by transferring sugar nucleotide donors onto acceptors. Challenging chemical synthesis impedes synthetic access to sugar nucleotides and limits the study of many glycosyltransferases. Enzymatic access to sugar nucleotides is a rapidly expanding avenue of research, limited only by the substrate specificity of the enzyme. We have explored the promiscuous thymidylyltransferase from Streptococcus pneumoniae, Cps2L, and enhanced its uridylyltransferase and guanidyltransferase activities by active site engineering. Mutagenesis at position Q24 resulted in a variant with 10-, 3-, and 2-fold enhancement of UDP-glucosamine, UDP-mannose, and UDP- N-acetylglucosamine production, respectively. New catalytic activities were observed for the Cps2L variant over the wild-type enzyme, including the formation of GDP-mannose. The variant was evaluated as a catalyst for the formation of a series of dTDP- and UDP-furanoses and notably produced dTDP-Gal f in 90% yield and UDP-Ara f in 30% yield after 12 h. A series of 3- O-alkylglucose 1-phosphates were also evaluated as substrates, and notable conversions to UDP-3- O-methylglucose and UDP-3- O-dodecylglucose were achieved with the variant but not the wild-type enzyme. The Q24S variant also enhanced essentially all thymidylyltransferase activities relative to the wild-type enzyme. Comparison of active sites of uridylyltransferases and thymidylyltransferases with products bound indicate the Q24S variant to be a new approach in broadening nucleotidylyltransferase activity.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".