Membrane Internalization Mechanisms and Design Strategies of Arginine-Rich Cell-Penetrating Peptides
Bibliographic record
Abstract
Cell-penetrating peptides (CPPs) have been discovered to deliver chemical drugs, nucleic acids, and macromolecules to permeate cell membranes, creating a novel route for exogenous substances to enter cells. Up until now, various sequence structures and fundamental action mechanisms of CPPs have been established. Among them, arginine-rich peptides with unique cell penetration properties have attracted substantial scientific attention. Due to the positively charged essential amino acids of the arginine-rich peptides, they can interact with negatively charged drug molecules and cell membranes through non-covalent interaction, including electrostatic interactions. Significantly, the sequence design and the penetrating mechanisms are critical. In this brief synopsis, we summarize the transmembrane processes and mechanisms of arginine-rich peptides; and outline the relationship between the function of arginine-rich peptides and the number of arginine residues, arginine optical isomers, primary sequence, secondary and ternary structures, etc. Taking advantage of the penetration ability, biomedical applications of arginine-rich peptides have been refreshed, including drug/RNA delivery systems, biosensors, and blood-brain barrier (BBB) penetration. Understanding the membrane internalization mechanisms and design strategies of CPPs will expand their potential applications in clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".