Mass Spectrometric Quantitation of Peptides and Proteins Using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA)
Bibliographic record
Abstract
A method (denoted SISCAPA) for quantitation of peptides in complex digests is described. In the method, anti-peptide antibodies immobilized on 100 nanoliter nanoaffinity columns are used to enrich specific peptides along with spiked stable-isotope-labeled internal standards of the same sequence. Upon elution from the anti-peptide antibody supports, electrospray mass spectrometry is used to quantitate the peptides (natural and labeled). In a series of pilot experiments, tryptic test peptides were chosen for four proteins of human plasma (hemopexin, alpha1 antichymotrypsin, interleukin-6, and tumor necrosis factor-alpha) from a pool of 10,203 in silico tryptic peptide candidates representing 237 known plasma components. Rabbit polyclonal antibodies raised against the chosen peptide sequences were affinity purified and covalently immobilized on POROS supports. Binding and elution from these supports was shown to provide an average 120-fold enrichment of the antigen peptide relative to others, as measured by selected ion monitoring (SIM) or selected reaction monitoring (SRM) electrospray mass spectrometry. The columns could be recycled with little loss in binding capacity, and generated peptide ion current measurements with cycle-to-cycle coefficients of variation near 5%. Anti-peptide antibody enrichment will contribute to increased sensitivity of MS-based assays, particularly for lower abundance proteins in plasma, and may ultimately allow substitution of a rapid bind/elute process for the time-consuming reverse phase separation now used as a prelude to online MS peptide assays. The method appears suitable for rapid generation of assays for defined proteins, and should find application in the validation of diagnostic protein panels in large sample sets.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".