Diversity-oriented synthesis of second generation guanidinium-rich transporters toward cell-selective penetration
Bibliographic record
Abstract
• A library of 256 peptidomimetics was synthesized using an automated peptide synthesizer. • The synthesized library exhibits selective internalization to Hela cells in comparison with HEK293 cells. • Compounds 155 and 187, show 2.7-fold and 2.9-fold higher in cellular uptake on HeLa cells compared to HEK293 cells, respectively. Cell-penetrating peptides (CPPs) hold significant promise for intracellular delivery of various cargo molecules such as therapeutics. However, the lack of selectivity remains a critical challenge and limits the clinical application of CPPs. Using an automated peptide synthesizer, we generated a diversity-oriented library of 256 peptidomimetics containing four modified peptoid guanidine-bearing monomers incorporated alternatively with four α-amino acids. These α-amino acids were chosen to enhance lipophilic interactions with the cell membrane (Phe, 2Nal) or to bear pH-sensitive properties (His), which could enhance cancer cell selectivity. The synthesized library exhibits selective internalization, with an average selectivity index (SI) of 1.49 for HeLa cells in comparison to non-cancerous HEK293 cells. Compounds 155 and 187 , containing three His residues and either Phe or 2Nal, show high cellular uptake in HeLa cells (64.6 % and 75.7 %, respectively) and possess an SI of 2.7 and 2.9, respectively, at the tested dose of 5 μM. Altogether, these findings highlight the use of diversity-oriented library synthesis to identify cell-permeable candidates as well as their potential for targeted cellular delivery and enhanced specificity.
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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 | 0.000 | 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 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".