Recent Developments in Computational Methods for De Novo Peptide Sequencing from Tandem Mass Spectrometry (MS/MS)
Bibliographic record
Abstract
Tandem mass spectrometry (MS/MS) has emerged as a major technology for peptide sequencing. Typically, there are three kinds of methods for the peptide sequencing: database searching, peptide tagging, and de novo sequencing. De novo sequencing has drawn increasing attention because of its independence from existing protein databases and potential for identifying new proteins, proteins resulting from mutations, proteins with unexpected modifications and so on. Recently, with the improvements in the accuracy of MS/MS and development of alternative fragmentation modes of MS/MS, many new de novo sequencing methods have been formulated. This paper reviews these recently developed sequencing methods including those for alternative MS/MS spectra. The paper first introduces background knowledge on peptide sequencing and mass spectrometry, and then reviews de novo peptide sequencing methods for traditional CID spectra. After that, it focuses on the recent development of de novo methods for alternative MS/MS spectra. In addition, methods using multiple spectra from the same peptide are surveyed. Finally, conclusions and some directions of future work are discussed.
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.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".