Methods to Characterise Enzyme Kinetics with Biological and Medicinal Substrates: The Case of Alkaline Phosphatase
Bibliographic record
Abstract
Abstract Alkaline phosphatase (AP) enzymes are of broad interest in fields ranging from biochemistry and medicine to biotechnology and nanotechnology. Characterising the catalytic activity of AP is typically realised by either employing non‐natural signal‐generating substrates that are detectable by absorbance and fluorescence spectroscopy or by quantifying the release of inorganic phosphate by the classic malachite green assay. The latter method is often required for studying “spectroscopically silent” biomolecular substrates, but it does not enable continuous monitoring of kinetics in real‐time. In recent years, newer techniques for studying AP function have been developed to circumvent this limitation, including fluorescent and colourimetric substrate‐specific assays based on supramolecular chemistry, organic probes and nanomaterials, as well as other assays based on isothermal titration calorimetry, direct detection with infrared spectroscopy and mass spectrometry, and monitoring conformational change by fluorescent nanoantennas. Here, we review these strategies and comment on their strengths and weaknesses in the context of AP.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".