Optimization and Changes in the Mode of Proteolytic Turnover of Quantum Dot–Peptide Substrate Conjugates through Moderation of Interfacial Adsorption
Bibliographic record
Abstract
Enzymes have many important roles in biology and industry, and proteases are one of the most important classes of enzymes. Semiconductor quantum dots (QDs) are attractive materials for developing protease activity probes because of their advantageous physical and optical properties; however, interactions between a protease and a QD conjugated with its substrate can affect the turnover of that substrate. Here, we study the turnover of multivalent QD-peptide substrate conjugates as a function of multiple parameters: (i) the ligand coating on the QD, including dihydrolipoic acid (DHLA), glutathione (GSH), DHLA-poly(ethylene glycol) (DHLA-PEG), and DHLA-zwitterionic sulfobetaine (DHLA-SB); (ii) the identity of the protease, including trypsin, thrombin, and plasmin; and (iii) the number of substrate and nonsubstrate biomacromolecules conjugated per QD. We show that limiting protease adsorption on QDs is critical for optimizing the turnover of conjugated peptide substrates. Protease adsorption is inhibitory, and very strong adsorption leads to an apparent "scooting" mode of activity with limited turnover. In contrast, with weaker adsorption, enhancements in the turnover rate likely result from a "hopping" mode of activity. The putative hopping mode is thought to feature processive turnover of all substrates in multivalent conjugates with a rate-limiting step of diffusion between individual conjugates, and the magnitude of such enhancements increases with decreases in adsorption. Although it was possible to passivate DHLA- and GSH-coated QDs with high densities of conjugated biomacromolecules, the most effective strategy for reducing adsorption was the substitution of these ligands. Whereas passivation incrementally increased turnover, DHLA-PEG and DHLA-SB ligands converted the mode of turnover with plasmin from scooting to hopping and the DHLA-SB enhanced the turnover rates with thrombin and trypsin by approximately an order of magnitude relative to GSH ligands. The new insights from the broad scope of this study provide an important framework for designing optimized QD conjugates as probes and sensors for enzyme 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 | 0.001 | 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 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".