Evaluation of Large Scale Quantitative Proteomic Assay Development Using Peptide Affinity-based Mass Spectrometry
Bibliographic record
Abstract
Stable isotope standards and capture by antipeptide antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution and detection by multiple reaction monitoring mass spectrometry to provide quantitative measurement of peptides as surrogates for their respective proteins. In this report, we describe a feasibility study to determine the success rate for production of suitable antibodies for SISCAPA assays in order to inform strategies for large-scale assay development. A workflow was designed that included a multiplex immunization strategy in which up to five proteotypic peptides from a single protein target were used to immunize individual rabbits. A total of 403 proteotypic tryptic peptides representing 89 protein targets were used as immunogens. Antipeptide antibody titers were measured by ELISA and 220 antipeptide antibodies representing 89 proteins were chosen for affinity purification. These antibodies were characterized with respect to their performance in SISCAPA-multiple reaction monitoring assays using trypsin-digested human plasma matrix. More than half of the assays generated were capable of detecting the target peptide at concentrations of less than 0.5 fmol/μl in human plasma, corresponding to protein concentrations of less than 100 ng/ml. The strategy of multiplexing five peptide immunogens was successful in generating a working assay for 100% of the targeted proteins in this evaluation study. These results indicate it is feasible for a single laboratory to develop hundreds of assays per year and allow planning for cost-effective generation of SISCAPA assays.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".