Charge state separation for protein applications using a quadrupole time‐of‐flight mass spectrometer
Bibliographic record
Abstract
A novel method for separating ions according to their charge state using a quadrupole time-of-flight mass spectrometer is presented. The benefits of charge state separation are particularly apparent in protein identification applications at low femtomole concentration levels, where in conventional TOF MS spectra peptide ions are often lost in a sea of chemical noise. When doubly and triply charged tryptic peptide ions need to be filtered from singly charged background ions, the latter are suppressed by two to three orders of magnitude, while from 10-50% of multiply charged ions remain. The suppression of chemical noise reduces the need for chromatography and can make this experimental approach the electrospray equivalent of conventional MALDI peptide maps. If unambiguous identification cannot be achieved, MS/MS experiments are performed on the precursor ions identified through charge separation, while the previously described Q2-trapping duty cycle enhancement is tuned for approximately 1.4 of the precursor m/z to enhance intensities of ions with m/z values above that of the precursor. The resulting product ion spectra contain few fragments of impurities and provide quick and unambiguous identification through database search. The multiple charge separation technique requires minimal tuning and may become a useful tool for analysis of complex mixtures.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".