Combinatorial Peptide Libraries Facilitate Development of Multiple Reaction Monitoring Assays for Low-Abundance Proteins
Bibliographic record
Abstract
Low-abundance proteins present in biological fluids are often considered an attractive source of new disease biomarkers. Since such proteins are poorly observed in proteome-scale discovery experiments due to an overwhelming mass of high-abundance proteins, the development of quantitative multiple reaction monitoring (MRM) assays for low-abundance proteins is a challenging task. Here, we present a strategy that facilitates the development of MRM assays for large numbers of unpurified low-abundance proteins. Our discovery strategy is based on the reduction of the dynamic range of protein concentrations in biological fluids by means of one-bead one-compound combinatorial peptide libraries (CPL). Our 2D-LC-MS/MS approach allowed us to identify a total of 484 unique proteins in ovarian cancer ascites, and 216 proteins were assigned as low-abundance ones. Interestingly, 74 of those proteins have never been previously described in ascites fluid. Treatment with CPL allowed identification of a significantly higher number of unique peptides for low-abundance proteins and provided important empirical fragmentation information for development of MRM assays. Finally, we confirmed that MRM assays worked for 30 low-abundance proteins in the unfractionated ascites digest. Using a multiplexed MRM method, relative amounts of five proteins (kallikrein 6, metalloproteinase inhibitor 1, macrophage migration inhibitory factor, follistatin-related protein, and mesothelin) were determined in a set of ovarian cancer ascites. Multiplexed MRM assays targeting large numbers of proteins can be used to develop comprehensive panels of biomarkers with high sensitivity and selectivity, and to study complex protein networks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".