Mass spectrometry in coupling with affinity capture–release and isotope‐coded affinity tags for quantitative protein analysis
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".