Probing the Impact of Valency on the Routing of Arginine-Rich Peptides into Eukaryotic Cells
Bibliographic record
Abstract
Multivalency represents a critical parameter in cell biology responsible for the overall avidity of low-affinity interactions and the triggering of cellular events. Functions such as catalytic activity, cellular uptake, or localization are frequently linked to the oligomeric state of a protein. This study explores the impact of multivalency on the import and routing of peptides into cells. Specifically, cationic import sequences such as decaarginine, decalysine, and the HIV Tat peptide (GRKKRRQRRRAP, residues 48-59) as well as the nuclear localization sequence from SV40 large T-antigen were assembled into defined peptide oligomers by fusing them to the tetramerization domain of human p53 (residues 325-355, hp53(tet) domain). The resulting tetravalent peptides typically displayed between 10- and 100-fold enhancements in cellular import and intracellular routing properties in relation to their monomeric homologues. These peptides were not toxic to cells. Flow cytometry results and transfection assays indicated that tetravalent decaarginyl peptides (10R-p53(tet) and NLS-10R-p53(tet)) were the peptides most efficiently routed into cells. Their mechanism of import was subsequently examined on unfixed, viable cells using a combination of metabolic inhibitors, flow cytometry, and microscopy techniques. These studies revealed that tetravalent arginine-rich peptides bind to heparan sulfate on the cell surface, are internalized at 37 degrees C, but not at 4 degrees C, via a clathrin-mediated pathway, and accumulate into endosome-like acidic compartments. A fraction of these tetravalent peptides access the cytosol and accumulate in the nucleus of cells. This study concludes that the oligomerization of proteins harboring arginine-rich peptide chains may profoundly influence their ability to enter and be routed into cells.
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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".