Applications of a matrix‐assisted laser desorption/ionization orthogonal time‐of‐flight mass spectrometer. l. Metastable decay and collision‐induced dissociation for sequencing peptides
Bibliographic record
Abstract
The use of a high-performance orthogonal time-of-flight (o-TOF) mass spectrometer for sequence analysis is described. The mass spectrometer is equipped with a matrix-assisted laser desorption/ionization (MALDI) source that operates at elevated pressure, 0.01-1 Torr. Ion fragmentation is controlled by varying the pressure of the buffer gas, the laser energy, the voltage difference between the MALDI target and the adjacent sampling cone, and between the cone and the quadrupole ion guide. The peptides were analyzed under optimal ionization conditions to obtain their molecular mass, and under conditions that promote ion dissociation via metastable decomposition or collision-induced dissociation (CID). The fragmentation spectra were used to obtain sequence information. Ion dissociation was promoted via three configurations of the ionization parameters. All methods yielded sequencing-grade b- and y-type ions. Two binary mixtures of peptides were used to demonstrate that: (1) external calibration provides a standard deviation (sigma) of 4 ppm with a mode of 9 ppm; and (2) that peptides with molecular masses that differ by a factor of two may be independently fragmented by appropriately choosing the CID energy and the low-mass cut-off. Analyses of tryptic digests employed liquid chromatography (LC), deposition of the eluant on a target, and finally MALDI-TOF mass spectrometry. The mass fingerprint and the (partial) sequence of the tryptic peptides were matched to their precursor protein via database searches.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".