Expanding Proteome Coverage with CHarge Ordered Parallel Ion aNalysis (CHOPIN) Combined with Broad Specificity Proteolysis
Bibliographic record
Abstract
The "deep" proteome has been accessible by mass spectrometry for some time. However, the number of proteins identified in cells of the same type has plateaued at ∼8000-10 000 without ID transfer from reference proteomes/data. Moreover, limited sequence coverage hampers the discrimination of protein isoforms when using trypsin as standard protease. Multienzyme approaches appear to improve sequence coverage and subsequent isoform discrimination. Here we expanded proteome and protein sequence coverage in MCF-7 breast cancer cells to an as yet unmatched depth by employing a workflow that addresses current limitations in deep proteome analysis in multiple stages: We used (i) gel-aided sample preparation (GASP) and combined trypsin/elastase digests to increase peptide orthogonality, (ii) concatenated high-pH prefractionation, and (iii) CHarge Ordered Parallel Ion aNalysis (CHOPIN), available on an Orbitrap Fusion (Lumos) mass spectrometer, to achieve 57% median protein sequence coverage in 13 728 protein groups (8949 Unigene IDs) in a single cell line. CHOPIN allows the use of both detectors in the Orbitrap on predefined precursor types that optimizes parallel ion processing, leading to the identification of a total of 179 549 unique peptides covering the deep proteome in unprecedented detail.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".