An algorithm for interpretation of low‐energy collision‐induced dissociation product ion spectra for <i>de novo</i> sequencing of peptides
Bibliographic record
Abstract
An algorithm for interpretation of product ion spectra of peptides generated from ion trap mass spectrometry is developed for de novo amino acid sequencing of peptides for the purpose of protein identification. It is based on a multi-pass analysis of product ion data using a rigorous data extraction and sequence interpretation protocol in the initial pass. The extraction/interpretation algorithm becomes more relaxed in subsequent passes, considering more of the fragment ions, and potentially more sequence candidates. The possible peptide sequences generated by the algorithm are scored according to those sequences which best explain the fragment ion spectrum. These sequences are searched against a protein database using a BLAST search engine to find likely protein candidates. The method is also suitable for locating and determining protein modifications, and can be applied to de novo interpretation of peptide fragment ions in the tandem mass (MS/MS) spectrum produced from a mixture of two peptides having similar nominal mass, but different sequences. Using a known protein, bovine serum albumin, as an example, it is illustrated that this method is rapid and efficient for MS/MS spectral interpretation. This method combined with BLAST programs is then applied to search homologies and to generate information on post-translational modifications of an unknown protein isolated from shark cartilage that does not have a complete genome or proteome database.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".