Bibliographic record
Abstract
Protein secondary structure can be mimicked by incorporating structural constraints into peptides and this can be facilitated by metal coordination. The objective of this project is to establish a chelation system with a cancer targeting peptide sequence, where the coordination to a metal centre results in a cyclic metallopeptide. Cyclic RGD peptides are antagonists for αvβ3 and other integrins, which are present during tumour angiogenesis. Arg-Gly-Asp (RGD) sequence is employed in the peptide backbone, with an un-natural amino acid (3-Pal) and a chelating molecule (pyridyl-triazole, pyta) present on opposite ends of the peptide sequence to form a linear peptide sequence, pyta-RGD-3-Pal-NH2. The linear peptide was reacted with [Re(OH2)3(CO)3]+ to form a cyclic system, with the pyridyl-triazole coordinating in a bidentate fashion. The linear peptide and resulting cyclic metallopeptide were characterized by high resolution mass spectrometry (HRMS), Circular Dichroism (CD) spectroscopy, one- and two-dimensional 1H-NMR spectroscopy, variable temperature (VT) NMR spectroscopy, with purity > 90% as determined by HPLC. Computational studies on the coordinated peptide suggested intra-molecular hydrogen bonging that were consistent with VT NMR data. The linear peptide was successfully radiolabelled with Tc-99m demonstrating the potential application as a SPECT imaging agent for angiogenesis. This approach to adding a structural constraint, through metal-based peptide cyclization, results in a metallopeptide where the metal is central to creating the turn mimetic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".