Cell-Penetrating Ability of Peptide Hormones: Key Role of Glycosaminoglycans Clustering
Bibliographic record
Abstract
Over the last two decades, the potential usage of cell-penetrating peptides (CPPs) for the intracellular delivery of various molecules has prompted the identification of novel peptidic identities. However, cytotoxic effects and unpredicted immunological responses have often limited the use of various CPP sequences in the clinic. To overcome these issues, the usage of endogenous peptides appears as an appropriate alternative approach. The hormone pituitary adenylate-cyclase-activating polypeptide (PACAP38) has been recently identified as a novel and very efficient CPP. This 38-residue polycationic peptide is a member of the secretin/glucagon/growth hormone-releasing hormone (GHRH) superfamily, with which PACAP38 shares high structural and conformational homologies. In this study, we evaluated the cell-penetrating ability of cationic peptide hormones in the context of the expression of cell surface glycosaminoglycans (GAGs). Our results indicated that among all peptides evaluated, PACAP38 was unique for its potent efficiency of cellular uptake. Interestingly, the abilities of the peptides to reach the intracellular space did not correlate with their binding affinities to sulfated GAGs, but rather to their capacity to clustered heparin in vitro. This study demonstrates that the uptake efficiency of a given cationic CPP does not necessarily correlate with its affinity to sulfated GAGs and that its ability to cluster GAGs should be considered for the identification of novel peptidic sequences with potent cellular penetrating properties.
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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".