Comparative analysis of photocaged RGDS peptides for cell patterning
Bibliographic record
Abstract
Photocaged RGDS is a cell nonadhesive tetrapeptide that can be activated with light to become cell-adhesive. Such molecules can find useful applications in controlling cell adhesion for biological study, drug development, and in forming dynamic, adhesion-controlled biomaterials. Herein, we prepared RGDS peptide photocaged either on the Arg-Gly backbone amide nitrogen atom (R[-]GDS) or Asp side chain carboxyl (RG[D]S). A critical comparison of the peptides' chemical and physiological properties relevant for biological applications was carried out. It was observed that RG[D]S was synthesized more readily via automated solid-phase synthesis, underwent uncaging with a rate constant 3-fold higher than R[-]GDS, and was more stable in aqueous solution. Automated docking studies were performed to examine the interactions of various caged RGDS peptides with cell surface integrin receptor to identify suitable locations for the photosensitive 2-nitrobenzyl (NB) group for biological applications. A competitive binding ELISA method compared the ability of various peptides to bind to α(V)β(3) cell integrin receptors and the data were found to be consistent with the modeling predictions. Finally, the application of our caged RGDS peptides in controlling cell adhesion to form cell patterns on a hydrogel material was presented.
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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".