Peptide Arrays for Screening Cancer Specific Peptides
Bibliographic record
Abstract
In this paper, we describe a novel method to screen peptides for specific recognition by cancer cells. Seventy peptides were synthesized on a cellulose membrane in an array format, and a direct method to study the peptide-whole cell interaction was developed. The relative binding affinity of the cells for different peptides with respect to a lead 12-mer p160 peptide, identified by phage display, was evaluated using the CyQUANT fluorescence of the bound cells. Screening allowed identification of at least five new peptides that displayed higher affinity (up to 3-fold) for MDA-MB-435 and MCF-7 human cancer cells compared to the p160 peptide. These peptides showed very little binding to the control (noncancerous) human umbilical vein endothelial cells (HUVECs). Three of these peptides were synthesized separately and labeled with fluorescein isothiocyanate (FITC) to study their uptake and interaction with the cancer and control cells using confocal laser scanning microscopy and flow cytometry. The results confirmed the high and specific affinity of an 11-mer peptide 11 (RGDPAYQGRFL) and a 10-mer peptide 18 (WXEAAYQRFL) for the cancer cells versus HUVECs. Peptide 11 binds different receptors on target cancer cells as its sequence contains multiple recognition motifs, whereas peptide 18 binds mainly to the putative p160 receptor. The peptide array-whole cell binding assay reported here is a complementary method to phage display for further screening and optimization of cancer targeting peptides for cancer therapy and diagnosis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".