Acceleration\nof Proteolytic Activity Associated with Selection of Thiol Ligand Coatings on Quantum Dots
Bibliographic record
Abstract
Nanoparticle bioconjugates are attractive\nprobes for measuring the activity of hydrolytic enzymes. In these\nconfigurations, the localization of multiple copies of a hydrolase\nsubstrate to a nanoparticle scaffold has been reported to enhance\napparent activity by factors of 2 to 3 compared to that for equivalent\namounts of substrate in bulk solution. Here, we studied the effect\nof surface chemistry on protease activity using multivalent QD–peptide\nsubstrate conjugates as a model system. QDs were coated with cysteine\n(CYS), glutathione (GSH), dihydrolipoic acid (DHLA), or 3-mercaptopropionic\nacid (MPA) ligands, and thrombin and trypsin were used as model proteases.\nProteolytic activity was measured for different combinations of ligand\nand protease using Förster resonance energy transfer (FRET)-based\nassays. The highest levels of activity were observed with CYS and\nGSH coatings, and the lowest levels of activity were observed with\nDHLA and MPA coatings. In all cases, proteolytic activity was accelerated\ncompared to that for an equivalent amount of substrate in bulk solution,\nwith up to 80- and 65-fold increases in the apparent specificity constants\nfor thrombin and trypsin, respectively. Thrombin was more strongly\naffected by the QD surface chemistry, with up to a 50-fold variation\nin its apparent specificity constant between ligand coatings, whereas\nonly a 5-fold variation was observed with trypsin. These trends were\ncorrelated to adsorption of the proteases on the QDs and are discussed\nin the context of the physicochemical properties of both components.\nThis work clearly indicates a critical role for the nanoparticle interface\nin mediating substrate turnover and provides some of the strongest\nsupport to date for a so-called hopping model of activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.076 | 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 teacher head, 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".