Exploiting Bacterial Glycosylation Machineries for the Synthesis of a Lewis Antigen-containing Glycoprotein
Bibliographic record
Abstract
Glycoproteins constitute a class of compounds of increasing importance for pharmaceutical applications. The manipulation of bacterial protein glycosylation systems from Gram-negative bacteria for the synthesis of recombinant glycoproteins is a promising alternative to the current production methods. Proteins carrying Lewis antigens have been shown to have potential applications for the treatment of diverse autoimmune diseases. In this work, we developed a mixed approach consisting of in vivo and in vitro steps for the synthesis of glycoproteins containing the Lewis x antigen. Using glycosyltransferases from Haemophilus influenzae, we engineered Escherichia coli to assemble a tetrasaccharide on the lipid carrier undecaprenylphosphate. This glycan was transferred in vivo from the lipid to a carrier protein by the Campylobacter jejuni oligosaccharyltransferase PglB. The glycoprotein was then fucosylated in vitro by a truncated fucosyltransferase from Helicobacter pylori. Diverse mass spectrometry techniques were used to confirm the structure of the glycan. The strategy presented here could be adapted in the future for the synthesis of diverse glycoproteins. Our experiments demonstrate that bacterial enzymes can be exploited for the production of glycoproteins carrying glycans present in human cells for potential therapeutic applications.
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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 | 0.001 |
| 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".