Investigation of sequon engineering for improved O-glycosylation by the human polypeptide N-acetylgalactosaminyl transferase T2 isozyme and two orthologues
Bibliographic record
Abstract
We have been developing bacterial expression systems for human mucin-type O-glycosylation on therapeutic proteins, which is initiated by the addition of α-linked GalNAc to serine or threonine residues by enzymes in the GT-27 family of glycosyltransferases. Substrate preference across different isoforms of this enzyme is influenced by isoform-specific amino acid sequences at the site of glycosylation, which we have exploited to engineer production of Core 1 glycan structures in bacteria on human therapeutic proteins. Using RP-HPLC with a novel phenyl bonded phase to resolve intact protein glycoforms, the effect of sequon mutation on O-glycosylation initiation was examined through in vitro modification of the naturally O-glycosylated human interferon α-2b, and a sequon engineered human growth hormone. As part of the development of our glycan engineering in the bacterial expression system we are surveying various orthologues of critical enzymes to ensure complete glycosylation. Here we present an in vitro enzyme kinetic profile of three related GT-27 orthologues on natural and engineered sequons in recombinant human interferon α2b and human growth hormone where we show a significant change in kinetic properties with the amino acid changes. It was found that optimizing the protein substrate amino acid sequence using Isoform Specific O-Glycosylation Prediction (ISOGlyP, http://isoglyp.utep.edu/index.php) resulted in a measurable increase in kcat/KM, thus improving glycosylation efficiency. We showed that the Drosophila orthologue showed superior activity with our human growth hormone designed sequons compared with the human enzyme.
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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) | 0.000 | 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".