A direct, continuous, sensitive assay for protein disulphide-isomerase based on fluorescence self-quenching
Bibliographic record
Abstract
PDI (protein disulphide-isomerase) activity is generally monitored by insulin turbidity assay or scrambled RNase assay, both of which are performed by UV-visible spectroscopy. In this paper, we present a sensitive fluorimetric assay for continuous determination of disulphide reduction activity of PDI. This assay utilizes the pseudo-substrate diabz-GSSG [where diabz stands for di-(o-aminobenzoyl)], which is formed by the reaction of isatoic anhydride with the two free N-terminal amino groups of GSSG. The proximity of two benzoyl groups leads to quenching of the diabz-GSSG fluorescence by approx. 50% in comparison with its non-disulphide-linked form, abz-GSH (where abz stands for o-aminobenzoyl). Therefore the PDI-dependent disulphide reduction can be monitored by the increase in fluorescence accompanying the loss of proximity-quenching upon conversion of diabz-GSSG into abz-GSH. The apparent K(m) of PDI for diabz-GSSG was estimated to be approx. 15 muM. Unlike the insulin turbidity assay and scrambled RNase assay, the diabz-GSSG-based assay was shown to be effective in determining a single turnover of enzyme in the absence of reducing agents with no appreciable blank rates. The assay is simple to perform and very sensitive, with an estimated detection limit of approx. 2.5 nM PDI, enabling its use for the determination of platelet surface PDI activity in crude sample preparations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".