Quantifying Missing (Phospho)Proteome Regions with the Broad-Specificity Protease Subtilisin
Bibliographic record
Abstract
Despite huge efforts to map the human proteome using mass spectrometry the overall sequence coverage achieved to date is still below 50%. Reasons for missing areas of the proteome comprise protease-resistant domains including the lack/excess of enzymatic cleavage sites, nonunique peptide sequences, impaired peptide ionization/separation and low expression levels. To access novel areas of the proteome the beneficial use of enzymes complementary to trypsin, such as Glu-C, Asp-N, Lys-N, Arg-C, LysargiNase has been reported. Here, we present how the broad-specificity protease subtilisin enables mapping of previously hidden areas of the proteome. We systematically evaluated its digestion efficiency and reproducibility and compared it to the gold standard in the field, trypsin. Notably, subtilisin allows reproducible near-complete digestion of cells lysates in 1-5 min. As expected from its broad specificity the generation of overlapping peptide sequences reduces the number of identified proteins compared to trypsin (8363 vs 6807; 1% protein FDR). However, subtilisin considerably improved the coverage of missing and particularly proline-rich areas of the proteome. Along 14 628 high confidence phosphorylation sites identified in total, only 33% were shared between both enzymes, while 37% were exclusive to subtilisin. Notably, 926 of these were not even accessible by additional in silico digestion with either Asp-N, Arg-C, Glu-C, Lys-C, or Lys-N. Thus, subtilisin might be particularly beneficial for system-wide profiling of post-translational modification sites. Finally, we demonstrate that subtilisin can be used for reporter-ion based in-depth quantification, providing a precision comparable to trypsin-despite broad specificity and fast digestion that may increase technical variance.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".