Polyamine Analogues Bind Human Serum Albumin
Bibliographic record
Abstract
Polyamine analogues show antitumor activity in experimental models, and their ability to alter activity of cytotoxic chemotherapeutic agents in breast cancer is well documented. Association of polyamines with nucleic acids and protein is included in their mechanism of action. The aim of this study was to examine the interaction of human serum albumin (HSA) with several polyamine analogues, such as 1,11-diamino-4,8-diazaundecane (333), 3,7,11,15-tetrazaheptadecane.4HCl (BE-333), and 3,7,11,15,19-pentazahenicosane.5HCl (BE-3333), in aqueous solution at physiological conditions using a constant protein concentration and various polyamine contents (microM to mM). FTIR, UV-visible, and CD spectroscopic methods were used to determine the polyamine binding mode and the effects of polyamine complexation on protein stability and secondary structure. Structural analysis showed that polyamines bind nonspecifically (H-bonding) via polypeptide polar groups with binding constants of K333 = 9.30 x 10(3) M(-1), KBE-333 = 5.63 x 10(2) M(-1), and KBE-3333 = 3.66 x 10(2) M(-1). The protein secondary structure showed major alterations with a reduction of alpha-helix from 55% (free protein) to 43-50% and an increase of beta-sheet from 17% (free protein) to 29-36% in the 333, BE-333, and BE-3333 complexes, indicating partial protein unfolding upon polyamine interaction. HSA structure was less perturbed by polyamine analogues compared to those of the biogenic polyamines.
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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".