Multiplex Detection of Protease Activity with Quantum Dot Nanosensors Prepared by Intein-Mediated Specific Bioconjugation
Bibliographic record
Abstract
We report here a protease sensing nanoplatform based on semiconductor nanocrystals or quantum dots (QDs) and bioluminescence resonance energy transfer (QD-BRET) to detect the protease activity in complex biological samples. These nanosensors consist of bioluminescent proteins as the BRET donor, quantum dots as the BRET acceptor, and protease substrates sandwiched between the two as a sensing group. An intein-mediated conjugation strategy was developed for site-specific conjugation of proteins to QDs in preparing these QD nanosensors. In this traceless ligation, the intein itself is spliced out and excluded from the final conjugation product. With this method, we have synthesized a series of QD nanosensors for highly sensitive detection of an important class of protease matrix metalloproteinase (MMP) activity. We demonstrated that these nanosensors can detect the MMP activity in buffers and in mouse serum with the sensitivity to a few nanograms per milliliter and secreted proteases by tumor cells. The suitability of these nanosensors for a multiplex protease assay has also been shown.
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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) | 0.000 | 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".