De Novo Peptide Sequencing by Nanoelectrospray Tandem Mass Spectrometry Using Triple Quadrupole and Quadrupole/Time-of-Flight Instruments
Bibliographic record
Abstract
Recent developments in technology and instrumentation have made mass spectrometry the method of choice for the identification of gel-separated proteins using rapidly growing sequence databases ( 1 ). Proteins with a full-length sequence present in a database can be identified with high certainty and high throughput using the accurate masses obtained by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry peptide mapping ( 2 ). Simple protein mixtures can also be deciphered by MALDI peptide mapping ( 3 ) and the entire identification process, starting from in-gel digestion ( 4 ) and finishing with acquisition of mass spectra and database search, can be automated ( 5 ). Only 1–3% of a total digest are consumed for MALDI analysis even if the protein of interest is present on a gel in a subpicomole amount. If no conclusive identification is achieved by MALDI peptide mapping, the remaining protein digest can be analyzed by nanoelectrospray tandem mass spectrometry (Nano ESI-MS/MS) ( 6 ). Nano ESI-MS/MS produces data that allow highly specific database searches so that proteins that are only partially present in a database, or relevant clones in an EST database, can be identified ( 7 ). It is important to point out that there is no need to determine the complete sequence of peptides in order to search a database-a short sequence stretch consisting of three to four amino acid residues provides enough search specificity when combined with the mass of the intact peptide and the masses of corresponding fragment ions in a peptide sequence tag ( 8 ) ( see Subheading 3.4. ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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".