Cleavage of Disulfide-Linked Fetuin−Bisphosphonate Conjugates with Three Physiological Thiols
Bibliographic record
Abstract
An effective therapeutic agent for treatment of bone diseases is expected to exhibit a high affinity to bone. Conjugating proteins to bisphosphonates (BPs), a class of molecules with an exceptional affinity to bone mineral hydroxyapatite (HA), is a feasible means to impart such a bone affinity. Protein-BP conjugates with cleavable linkages, which allow protein release from the mineral, are preferable over conjugates with stable linkages. To this end, 2-(3-mercaptopropylsulfanyl)-ethyl-1,1-bisphosphonic acid (thiolBP) was conjugated onto fetuin, a model protein, using N-succinimidyl-3-(2-pyridyldithio)propionate to create disulfide-linked conjugates. Although the fetuin-thiolBP conjugates were stable under aqueous conditions, the disulfide linkage was readily cleaved in the presence of the physiological thiols l-cysteine, dl-homocysteine, and l-glutathione. dl-Homocysteine exhibited the highest cleavage of the disulfide linkage among these thiols. The imparted bone affinity as a result of thiolBP conjugation, as assessed by HA binding in vitro, was eliminated upon cleavage of the disulfide linkage. The cleavage of the conjugates bound to HA was as effective as the conjugate cleavage in solution, and even more so at high concentrations of l-glutathione. In conclusion, disulfide-linked fetuin-thiolBP conjugates exhibited a high affinity to HA, which was readily lost upon cleavage with thiols found in physiological milieu.
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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 | 0.000 |
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".