Penetratin and Related Cell-Penetrating Cationic Peptides Can Translocate Across Lipid Bilayers in the Presence of a Transbilayer Potential
Bibliographic record
Abstract
Fluorescent-labeled derivatives of the Antennapedia-derived cell-penetating peptide penetratin, and of the simpler but similarly charged peptides R(6)GC-NH(2) and K(6)GC-NH(2), are shown to be able to translocate into large unilamellar lipid vesicles in the presence of a transbilayer potential (inside negative). Vesicles with diverse lipid compositions, and combining physiological proportions of neutral and anionic lipids, are able to support substantial potential-dependent uptake of all three cationic peptides. The efficiency of peptide uptake under these conditions is strongly modulated by the vesicle lipid composition, in a manner that suggests that more than one mechanism of peptide uptake may operate in different systems. Remarkably, peptide uptake is accompanied by only minor perturbations of the overall barrier function of the lipid bilayer, as assessed by assays of vesicle leakiness under the same conditions. Fluorescence microscopy of living CV-1 and HeLa cells incubated with the labeled peptides shows that the peptides accumulate in peripheral vesicular structures at early times of incubation, consistent with an initial endosomal localization as recently reported, but gradually accumulate in the cytoplasm and nucleus during more extended incubations (several hours). Our findings indicate that these relatively hydrophilic, polybasic cell-penetrating peptides can translocate through lipid bilayers by a potential- and composition-dependent pathway that causes only minimal perturbation to the overall integrity and barrier function of the bilayer.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".