A Vascular Endothelial Growth Factor High Affinity Receptor 1-specific Peptide with Antiangiogenic Activity Identified Using a Phage Display Peptide Library
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) is known to play a predominant role in tumor angiogenesis and metastasis formation that is mediated by its interactions with two tyrosine kinase receptors, VEGFRI (Flt-1) and VEGFRII (KDR). Inhibition of VEGF-dependent events in tumor tissues is known to enhance apoptosis and to suppress tumor growth. A novel peptide, SP5.2, which selectively binds Flt-1 and inhibits a broad range of VEGF-mediated events, was identified using a phage-display library screening. The fluorescein-labeled SP5.2 specifically bound to VEGF-stimulated primary human cerebral endothelial cells (HCECs), whereas non-stimulated HCECs, as well as human neuroblastoma cells (ShyY) did not show any interaction with the peptide. SP5.2 prevented proliferation of cultured primary human umbilical vein endothelial cells induced by recombinant human VEGF165 with an IC50 of 5 microm. SP5.2 was also shown to antagonize VEGF- and PLGF-induced, but not basic fibroblast growth factor-induced proliferation of HCECs. In contrast to "scrambled" peptide, SP5.2 was also found to selectively inhibit VEGF-stimulated migration of HCECs. The in vitro analysis of antiangiogenic activity of SP5.2 using a capillary-like tube formation assay showed that VEGF-induced angiogenesis of HCECs grown on Matrigel was completely inhibited in the presence of 10 microm SP5.2. Further studies demonstrated that SP5.2 prevented VEGF-induced permeability increase in HCECs monolayers. To explore whether SP5.2 can be used as a targeting agent, chemical and recombinant conjugates of SP5.2 with reporter proteins (peroxidase and beta-galactosidase) were produced. The resulting products showed significant increases (200-fold for SP5.2-beta-gal and 400-fold for SP5.2-peroxidase) in binding affinity to recombinant Flt-1 compared with the original synthetic SP5.2, suggesting that conjugate with therapeutic activity in nanomolar range could potentially be developed based on SP5.2 structure.
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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) | 0.001 | 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".