Impacts of PEGylation and Glycosylation on the Biological Properties of Host Defense Peptide IDR1018
Bibliographic record
Abstract
The multifunctional properties of host defense peptides (HDPs) make them promising drug candidates to tackle bacterial infections and tissue inflammation. However, these peptides tend to aggregate and can harm host cells at high doses, potentially limiting their clinical use and applications. In this study, we explored the influences of both pegylation and glycosylation on the biocompatibility and biological properties of HDPs, particularly the innate defense regulator IDR1018. Two peptide conjugates were designed by attaching either polyethylene glycol (PEG6) or a glucose moiety to the peptide towards the N-terminus. Significantly, both derivatives reduced the aggregation, hemolysis, and cytotoxicity of the parent peptide by orders of magnitude. In addition, while the pegylated conjugate, PEG6-IDR1018, retained an excellent immunomodulatory profile, similar to that observed for IDR1018 itself, the glycosylated conjugate, Glc-IDR1018, significantly outperformed the parent peptide in inducing anti-inflammatory mediators, MCP1 and IL-1RA and in suppressing the level of lipopolysaccharide-induced proinflammatory cytokine IL-1β. Conversely, the conjugates led to a partial reduction in antimicrobial and antibiofilm activity. These findings underline the impacts of both pegylation and glycosylation on the biological properties of the HDP IDR1018 and indicate the potential of glycosylation to enhance the design of highly effective immunomodulatory peptides.
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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".