NGR Peptide Ligands for Targeting CD13/APN Identified through Peptide Array Screening Resemble Fibronectin Sequences
Bibliographic record
Abstract
Peptides containing the Asn-Gly-Arg (NGR) motif are known to bind CD13 isoforms expressed in tumor vessels and have been widely used for tumor targeting. Residues flanking the NGR sequence play an important role in modulating the binding affinity and specificity of NGR for the CD13 receptor. Herein, we have used a rapid, easy, and reliable peptide array-whole cell binding assay for screening a library of NGR peptides with different flanking residues. A peptide array consisting of forty-five NGR containing peptides was synthesized on a cellulose membrane, followed by screening against CD13 positive (HUVEC and HT-1080) and CD13 negative cell lines (MDA-MB-435 and MDA-MB-231). The library screening led to the identification of five cyclic and acyclic NGR peptides that display higher binding (up to 5-fold) to CD13 positive cells with negligible binding to CD13 negative cell lines when compared to the lead sequence cyclic CVLNGRMEC. Peptides with high binding affinity for the CD13 positive cells also showed improved in vitro cellular uptake and specificity using flow cytometry and fluorescence microscopy. Interestingly, the identified peptides resemble the NGR sequences present in the human fibronectin protein. These NGR peptides are promising new ligands for developing tumor vasculature targeted drugs, delivery systems and imaging agents with reduced systemic toxicity.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".