Automated protein (re)sequencing with MS/MS and a homologous database yields almost full coverage and accuracy
Bibliographic record
Abstract
Abstract Motivation: The bottom-up tandem mass spectrometry (MS/MS) is regularly used in proteomics nowadays for identifying proteins from a sequence database. De novo sequencing software is also available for sequencing novel peptides with relatively short sequence lengths. However, automated sequencing of novel proteins from MS/MS remains a challenging problem. Results: Very often, although the target protein is novel, it has a homologous protein included in a known database. When this happens, we propose a novel algorithm and automated software tool, named Champs, for sequencing the complete protein from MS/MS data of a few enzymatic digestions of the purified protein. Validation with two standard proteins showed that our automated method yields >99% sequence coverage and 100% sequence accuracy on these two proteins. Our method is useful to sequence novel proteins or ‘re-sequence’ a protein that has mutations comparing with the database protein sequence. Availability: The software, named Champs (Complete Homology-Assisted Ms/ms Protein Sequencing), and the MS/MS data used in the article, are freely available at http://monod.uwaterloo.ca/champs/. Contact: binma@uwaterloo.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".