Fluorinating the Sugar and the Nucleotide: Exploring Fluorination Within GDP-Mannose Probes Using Chemoenzymatic Synthesis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Fluorinated glycans offer a prime opportunity to study the intricacies of their associated binding events with proteins, invoke resistance toward enzymatic hydrolysis, and modulate carbohydrate physicochemical properties. Sugar nucleotides are the key building blocks used by glycosyltransferases and associated enzymes to assemble glycans and, as such, represent a considerable landscape of opportunity to develop fluorinated motifs and enable structure-to-function understanding. Herein, we target the isosteric inclusion of fluorine within the nucleoside diphosphate sugar framework of GDP-mannose using a chemoenzymatic approach. Utilizing chemical synthesis to incorporate bespoke fluorine modifications and a promiscuous pyrophosphorylase, to assemble the sugar nucleotide, enables first-in-class access to GDP-mannoses containing fluorine within the nucleotide alongside double fluorination, within both the pyranose and nucleotide. These materials are utilized to probe a guanosine diphosphate mannose dehydrogenase critical to mucoid Pseudomonas aeruginosa alginate biosynthesis. This work provides an exemplar framework for incorporating fluorine within the nucleotide of derived sugar nucleotides and thus the capability to study glycosyltransferesase utilizing GDP-mannose more broadly.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".