Mass spectrometry in coupling with affinity capture–release and isotope‐coded affinity tags for quantitative protein analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Affinity capture-release electrospray ionization mass spectrometry (ACESIMS) and isotope-coded affinity tags (ICAT) are two recently introduced techniques for the quantitation of protein activity and content with applications to clinical enzymology and functional proteomics, respectively. One common feature of these methods is that they use biotinylated tags that function as molecular handles for highly selective and reversible affinity capture of conjugates from complex biological mixtures such as cell homogenates and sub-cellular organelles. ACESIMS uses synthetic substrate conjugates specifically to target cellular enzymes that, when deficient, are the cause of genetic diseases. Multiplex determination of enzyme activities is used for the diagnosis of lysosomal storage diseases. The ICAT method relies on selective conjugation of cysteine thiol groups in proteins, followed by enzymatic digestion and quantitative analysis of peptide conjugates by mass spectrometry. Another common feature of the ACESIMS and ICAT approaches is that both use conjugates labeled with stable heavy isotopes as internal standards for quantitation. Selected applications of the ACESIMS and ICAT techniques are presented that include molecular-level diagnosis of genetic diseases in children and quantitative determination of protein expression in cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it