Acceleration of Proteolytic Activity Associated with Selection of Thiol Ligand Coatings on Quantum Dots
Bibliographic record
Abstract
Nanoparticle bioconjugates are attractive probes for measuring the activity of hydrolytic enzymes. In these configurations, the localization of multiple copies of a hydrolase substrate to a nanoparticle scaffold has been reported to enhance apparent activity by factors of 2 to 3 compared to that for equivalent amounts of substrate in bulk solution. Here, we studied the effect of surface chemistry on protease activity using multivalent QD-peptide substrate conjugates as a model system. QDs were coated with cysteine (CYS), glutathione (GSH), dihydrolipoic acid (DHLA), or 3-mercaptopropionic acid (MPA) ligands, and thrombin and trypsin were used as model proteases. Proteolytic activity was measured for different combinations of ligand and protease using Förster resonance energy transfer (FRET)-based assays. The highest levels of activity were observed with CYS and GSH coatings, and the lowest levels of activity were observed with DHLA and MPA coatings. In all cases, proteolytic activity was accelerated compared to that for an equivalent amount of substrate in bulk solution, with up to 80- and 65-fold increases in the apparent specificity constants for thrombin and trypsin, respectively. Thrombin was more strongly affected by the QD surface chemistry, with up to a 50-fold variation in its apparent specificity constant between ligand coatings, whereas only a 5-fold variation was observed with trypsin. These trends were correlated to adsorption of the proteases on the QDs and are discussed in the context of the physicochemical properties of both components. This work clearly indicates a critical role for the nanoparticle interface in mediating substrate turnover and provides some of the strongest support to date for a so-called hopping model of activity.
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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".