Multivariate analysis of single quadrupole LC‐MS spectra for routine characterization and quantification of intact proteins
Bibliographic record
Abstract
Modern high-throughput proteomic platforms allow incomparable protein mixture resolution and identification. However, such sophisticated facilities are expensive and not always accessible for routine analysis of simple mixtures. In this paper, we propose a simple methodology, based on detection of intact, nondigested proteins by LC coupled to single quadrupole MS (sqLC-MS), followed by the analysis of the resulting spectra by multivariate analysis (MA). By doing so, even large molecular weight (MW) proteins, generating complex spectra, can be characterized to a level that allows isoform discrimination, while standard algorithms, such as MS spectrum deconvolution, cannot. To demonstrate the effectiveness of the proposed approach, we have analyzed the spectra of a set of purified, intact albumins from seven different organisms (bovine, human, rabbit, rat, sheep, mouse, and pig) as a model of microheterogenous proteins, using Projection to Latent Structure Discriminant Analysis (PLS-DA). Although these proteins are very similar (less than 1% difference in MW), sqLC-MS/MA allowed their classification, and the identification of unknown source samples. In addition, MA allowed precise protein quantification from the same data (calibration curve R2 = 0.9966). The ability to rapidly characterize and quantify proteins, together with simplicity and affordability, could make of combined sqLC-MS/MA a routine method for the characterization of simple mixture of known proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 teacher head, 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".