Masking terminal neo-epitopes of linear peptides through glycosylation favours immune responses towards core epitopes producing parental protein bound antibodies
Bibliographic record
Abstract
Glycosylation of hydrophobic peptides at one terminus effectively increases their water-solubility, and conjugation through the opposing end to a carrier protein, renders them more immunogenic. Moreover, the glycosylation minimizes antibody responses to potentially deleterious, non-productive terminal neo-epitope regions of the peptides, and consequently shifts peptide immunogenicity towards the core amino acid residues. As proof of concept, glycopeptide-protein conjugates related to influenza hemagglutinin (HA), neuraminidase (NA), and the dimerization loop region of human epidermal growth factor receptor 2 (Her2), demonstrated a favorable production of core peptide specific antibodies as determined by ELISA studies. Furthermore, glycosylated Her2 peptide conjugate antisera were also shown to recognize full length Her2 protein by ELISA and at the cell surface through flow cytometry analysis. In contrast, unmasked peptide conjugates generated significant antibody populations that were specific to the terminal neo-epitope of the peptide immunogen that are notably absent in parental proteins. Antibodies generated in this manner to peptides in the dimerization loop of Her2 are also functional as demonstrated by the growth inhibition of Her2 expressing SKBR3 carcinoma cells. This method provides a technique to tailor-make epitope-specific antibodies that may facilitate vaccine, therapeutic and diagnostic antibody development.
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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".