Kinetic characterization of ribonuclease S mutants containing photoisomerizable phenylazophenylalanine residues
Bibliographic record
Abstract
Incorporation of the photoisomerizable amino acid phenylazophenylalanine (PAP) into enzyme structures has been proposed as a strategy for photoswitching enzyme activity. To evaluate the strengths and limitations of this approach to enzyme photo-control, we performed a kinetic analysis of RNase S analogues containing PAP in positions 4, 7, 8, 10, 11 or 13. For an enzyme containing a single PAP group, the maximum extent of photoconversion (between approximately 96% trans/4% cis and 10% trans/90% cis under standard conditions) sets a limit on the maximum fold change in the initial rate of approximately 25-fold, if the cis form is the more active isomer, and approximately 10-fold if the trans form is more active. This extent of photoswitching was not realized in the present case because the effects of photoisomerization on kinetic constants were small and distributed among effects on S-peptide binding, substrate binding and the rate of the chemical step. These results suggest that photoisomerization could substantially alter enzyme kinetic constants but that a directed combinatorial approach might be required for realizing maximal photo-control in such systems. The limit set by the extent of photoconversion might be overcome by coupling multiple PAP groups to one enzyme or by altering the behaviour of a system that required oligomerization for activity.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".