Linear and Gaussian Analysis of a Single Enzyme Molecule by LC–MS
Bibliographic record
Abstract
Abstract The alkaline phosphatase–streptavidin enzyme amplification conjugate (APSA) was diluted and quantified to the equivalent of one enzyme molecule injected on column by monitoring the production of excess adenosine from adenosine monophosphate (AMP) using sensitive and selective enzyme-linked mass spectrometric assay. The APSA enzyme conjugate has a mass of about 195 kDa and catalyzed the production of millions of enzyme products over the course of incubation that may be sensitively quantified by liquid chromatography, electrospray ionization, and mass spectrometry. APSA enzyme conjugate from fg/mL to ag/mL alongside 0 g/mL (control) was incubated with the substrate 1 mM AMP for 2 h in free solution before collecting a 1 μL of sample of the enzyme product adenosine for injection and analysis by LC–MS. The enzyme product adenosine showed a Gaussian distribution after log10 transformation. The safe limit of detection and quantification was approximately 250 zg of APSA enzyme conjugate injected on column. A linear signal with acceptable error was observed at the mass of the enzyme product adenosine from 10 to 10000 zg of APSA enzyme conjugate injected, compared to controls without enzyme. It was possible to make a linear and Gaussian measurement to the single molecule range of the universal APSA enzyme amplification conjugate per micro liter injected with approximately 10% error. This study describes the first linear and Gaussian quantification of enzyme product from the equivalent of one enzyme conjugate molecule injected onto LC–MS for analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".