Activation Kinetics of Zipper Molecular Beacons
Bibliographic record
Abstract
Proteases play key roles in the regulation of normal cellular function, and thus, their deregulation leads to many disease states. Molecular beacons are promising protease-imaging probes for the detection and characterization of disease as well as for the evaluation of treatment. Inspired by this, we examined the efficiency of zipper molecular beacons (ZMBs) as imaging probes. First, we showed experimentally that the symmetrical ZMB (zip5e5r), bearing 5-arginine and 5-glutamate arms, is as efficient as the asymmetrical zip5e8r in enhancing cell uptake but without the dark toxicity exhibited by the asymmetric zipper. Also, zip5e5r was shown to dissociate more efficiently at pH’s greater than 5. Using a simple two-state binding model, we attributed this to a larger number of charge-pair conformations for zip5e8r. We then measured the ability of soluble matrix metalloproteinases (MMPs) to cleave zip5e5r, and compared their cleavage efficiency with the original photodynamic molecular beacon (PMB). Finally, as a first step toward understanding our observations quantitatively, we simulated the native structures of the peptides GPLGLARK and EGPLGLARRK with charged termini NH3(+) and COO(-) that approximate the PMB and ZMB (with one pair of arginine/glutamate electrostatic zipper), respectively. We concluded that inclusion of the zipper changes the native structure of the MBs, altering the cleavage efficiency of different MMPs.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".