Whole-Cell Protein Identification Using the Concept of Unique Peptides
Bibliographic record
Abstract
A concept of unique peptides (CUP) was proposed and implemented to identify whole-cell proteins from tandem mass spectrometry (MS/MS) ion spectra. A unique peptide is defined as a peptide, irrespective of its length, that exists only in one protein of a proteome of interest, despite the fact that this peptide may appear more than once in the same protein. Integrating CUP, a two-step whole-cell protein identification strategy was developed to further increase the confidence of identified proteins. A dataset containing 40,243 MS/MS ion spectra of Saccharomyces cerevisiae and protein identification tools including Mascot and SEQUEST were used to illustrate the proposed concept and strategy. Without implementing CUP, the proteins identified by SEQUEST are 2.26 fold of those identified by Mascot. When CUP was applied, the proteins bearing unique peptides identified by SEQUEST are 3.89 fold of those identified by Mascot. By cross-comparing two sets of identified proteins, only 89 common proteins derived from CUP were found. The key discrepancy between identified proteins was resulted from the filtering criteria employed by each protein identification tool. According to the origin of peptides classified by CUP and the commonality of proteins recognized by protein identification tools, all identified proteins were cross-compared, resulting in four groups of proteins possessing different levels of assigned confidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".