Selective Detection of Sugar Phosphates by Capillary Electrophoresis/Mass Spectrometry and Its Application to an Engineered <i>E. coli</i> Host
Bibliographic record
Abstract
Abstract A highly selective method employing capillary electrophoresis and electrospray mass spectrometry (CE‐ESMS) with precursor ion scanning for fragment ions characteristic of phosphate‐linked sugars was developed for the determination of “unnatural” sugar phosphates generated in vivo, as part of a natural product glycorandomization study. Cell lysates from an engineered E. coli host were probed for “natural” and “unnatural” sugar phosphates resulting from in vivo galactokinase (GalK) bioconversions, and tandem mass spectrometry experiments were performed to confirm the identities of the sugar phosphates. Among the 22 cell lysates that were studied, 13 were found to contain the expected natural and “unnatural” sugar phosphates. This was in agreement with the GalK in vitro conversion yields, in which an in vitro yield of ≤15 % coincided with a lack of observable in vivo bioconversion. In addition, the CE‐ESMS and precursor ion scanning method was capable of separating sugar phosphate regioisomers such as hexose‐6‐phosphate and hexose‐1‐phosphate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".