Design, synthesis, and validation of an in vitro platform peptide-whole cell screening assay using MTT reagent
Bibliographic record
Abstract
An in vitro platform to perform peptide screening against different cancer cell lines was designed. The strategy for this screening relied on the design and detection of high-affinity cancer-targeting peptides based on the sequences of NGR and P160. Evaluation of the best binding peptides was performed via incubation of the peptide array-bounded cells with MTT reagent, which is reduced to purple formazan in living cells and further quantified using an Elispot and Kodak imager. For proof of concept, a peptide library (132 spots, and 66 different peptides) was designed, synthesized, and screened against different cancer cell lines. The current strategy assists in the identification of positive and negative peptides as well as the relative binding between positive ones. Better binding peptide sequences of the NGR motif were demonstrated to show up to a 2.6-fold increase in CD13+ cell lines with insignificant binding to CD13− ones. Comparable results were observed for P160 peptide sequences, to which different peptides had increased binding, with an up to 3-fold increase relative to the native P160 peptide. Based on our results, new peptide sequences for cancer targeting were identified, and the developed strategy was applied to two different peptide libraries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".