Using 2D-LC-MS/MS to Identify Francisella tularensis Peptides in Extracts from an Infected Mouse Macrophage Cell Line
Bibliographic record
Abstract
Two dimensional nano-high-performance liquid chromatography (nanoHPLC) coupled directly to a high-resolution tandem mass spectrometer (2D-nLC-MS/MS) is an excellent method for analyzing very complex peptide mixtures, especially when the quantity of sample available for analysis is severely limited. We describe here a relatively simple 2D-nLC-MS/MS approach that we often use to characterize complex peptide mixtures, such as those produced by the proteolytic digestion of protein extracts. A peptide mixture is resolved in the first dimension by stepped elution from a strong cation exchange (IEC) column and in the second dimension by reverse phase (RP) nanoHPLC chromatography prior to electrospray ionization. The peptide ions are analyzed by automatic tandem mass spectrometry in a hybrid quadrupole time-of-flight mass spectrometer (Q-TOF). In this chapter, we illustrate this approach by way of an example featuring analyses of peptides extracted from a mouse macrophage cell line infected with the hve vaccine strain of Francisella tularensis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".