Orientation and helical conformation of a tissue‐specific hunter‐killer peptide in micelles
Bibliographic record
Abstract
Hunter-killer peptides are chimeric synthetic peptides that selectively target specific cell types for an apoptotic death. These peptides, which are models for potential therapeutics, contain a homing sequence for receptor-mediated interactions and a pro-apoptotic sequence. Homing domains have been designed to target angiogenic tumor cells, prostate cells, arthritic tissue and, most recently, adipose tissue. After a receptor-mediated internalization, the apoptotic sequence, which contains D-enantiomer amino acids, initiates apoptosis through mitochondrial membrane disruption. We have begun structure and functional studies on a peptide (HKP1) that specifically targets angiogenic tumor cells for apoptosis. As a model for mitochondrial membrane disruption, we have examined peptide-induced leakage of a calcein fluorophore from large unilamellar vesicles. These experiments demonstrate more potent leakage activity by HKP1 than the peptide lacking the homing domain. Circular dichroism and 2D homonuclear NMR experiments demonstrate that this tumor-specific HKP adopts a left-handed amphipathic helix in association with dodecylphosphorylcholine micelles in a parallel orientation to the lipid-water interface with the homing domain remaining exposed to solvent. The amphipathic helix of the apoptotic domain orients with nonpolar leucine and alanine residues inserting most deeply into the lipid environment.
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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) | 0.000 | 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".