Whole-Cell Protein Identification Using the Concept of Unique Peptides
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it