Increasing peptide identification in tandem mass spectrometry through automatic function switching optimization
Bibliographic record
Abstract
Comprehensive proteomic studies that employ MS directed peptide sequencing are limited by optimal peptide separation and MS and tandem MS data acquisition routines. To identify the optimal parameters for data acquisition, we developed a system that models the automatic function switching behavior of a mass spectrometer using an MS-only dataset. Simulations were conducted to characterize the number and the quality of simulated fragmentation as a function of the data acquisition routines and used to construct operating curves defining tandem mass spectra quality and the number of peptides fragmented. Results demonstrated that one could optimize for quality or quantity, with the number of peptides fragmented decreasing as quality increased. The predicted optimal operating curve indicated that significant improvements can be realized by selecting the appropriate data acquisition parameters. The simulation results were confirmed experimentally by testing 10 LC MS/MS data acquisition parameter sets on an LC-Q-TOF-MS. Database matching of the experimental fragmentation returned peptide scores consistent with the predictions of the model. The results of the simulations of mass spectrometer data acquisition routines reveal an inverse relationship between the quality and the quantity of peptide identifications and predict an optimal operating curve that can be used to select an optimal data acquisition parameter for a given (or any) sample.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".