Information‐dependent <scp>LC</scp>‐<scp>MS</scp>/<scp>MS</scp> acquisition with exclusion lists potentially generated on‐the‐fly: Case study using a whole cell digest of <i><scp>C</scp>lostridium thermocellum</i>
Bibliographic record
Abstract
We have developed a real-time graphic-processor-unit-based search engine capable of high-quality peptide identifications in <500 μs per spectrum. The steps of peptide/protein identification, in-silico prediction of all possible tryptic peptides from these proteins, and the prediction of their expected retention times and m/z values take less than 5 s per cycle over ∼3000 MS/MS spectra. This lays the foundation for information-dependent acquisition with exclusion lists generated on-the-fly, as the instrument continues to acquire data. While a complete evaluation of the dynamic exclusion system requires the participation from instrument vendors, we conducted a series of model experiments using a whole cell tryptic digestion of the bacterium Clostridium thermocellum. We ran a series of five iterative LC-MS/MS runs, adding a new exclusion list at each of four chromatographic "tripping points" - the elution times of the four standard peptides spiked into the sample. Retention times of these standard peptides were also used for real-time "chromatographic calibration." The dynamic exclusion approach gave a ≈ 5% increase in confident protein identification (for typical 2 h LC-MS/MS run), and reduced the average number of identified peptides per protein from 4.7 to 2.9. Its application to a two-times shorter gradient gave a ≈ 17% increase in proteins identified. Further improvements are possible for instruments with better mass accuracy, by employing a more accurate retention prediction algorithm and by developing better understanding of the possible chemical modifications and fragmentations produced during electrospray ionization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".