Protein-induced fluorescence enhancement for a simple and universal detection of protein/small molecule interactions
Bibliographic record
Abstract
We herein describe a novel and efficient method for the detection of protein/small molecule (SM) interactions, which relies on the protein-induced fluorescence enhancement (PIFE). In this method, a duplex probe is designed to position Cy3 and SM at the optimal distance to maximize the effect of PIFE, which is utilized as the key component. In the presence of target proteins that bind to SM, the Cy3 is guided close to the target proteins, which significantly enhances the fluorescence signal through a process of PIFE. With this approach, we successfully analyzed a model target protein, streptavidin (STV) that interacts with biotin (BTN) in less than 10 min without any washing steps. In addition, the practical applicability of this method was demonstrated by reliably determining STV in human serum. Finally, the universal applicability of this method was demonstrated by monitoring the interaction between folate and folate receptors.
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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 | 0.000 |
| 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".