Evaluation of the CFP-substrate-YFP system for protease studies: advantages and limitations
Bibliographic record
Abstract
A protease can be defined as an enzyme capable of hydrolyzing peptide bonds. Thus, characterization of a protease involves identification of target peptide sequences, measurement of activities toward these sequences, and determination of kinetic parameters. Biological protease substrates based on fluorescent protein pairs, which allow for use of fluorescence resonance energy transfer (FRET), have been recently developed for in vivo protease activity detection and represent a very interesting alternative to chemical substrates for in vitro protease characterization. Here, we analyze a FRET system consisting of cyan and yellow fluorescent proteins (CFP and YFP, respectively), which are fused by a peptide linker serving as protease substrate. Conditions for CFP-YFP fusion protein production in Escherichia coli and purification of proteins were optimized. FRET between CFP and YFP was found to be optimum at a pH between 5.5 and 10.0, at low concentrations of salt and a temperature superior to 25 degrees C. For efficient FRET to occur, the peptide linker between CFP and YFP can measure up to 25 amino acids. The CFP-substrate-YFP system demonstrated a high degree of resistance to nonspecific proteolysis, making it suitable for enzyme kinetic analysis. As with chemical substrates, substrate specificity of CFP-substrate-YFP proteins was tested towards different proteases and kcat/Km values were calculated.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".