Use of Peptide Retention Time Prediction for Protein Identification by off-line Reversed-Phase HPLC−MALDI MS/MS
Bibliographic record
Abstract
A new algorithm, sequence-specific retention calculator, was developed to predict retention time of tryptic peptides during RP HPLC fractionation on C18, 300-A pore size columns. Correlations of up to approximately 0.98 R2 value were obtained for a test library of approximately 2000 peptides and approximately 0.95-0.97 for a variety of real samples. The algorithm was applied in conjunction with an exclusion protocol based on mass (15 ppm tolerance) and retention time (2-min tolerance for 0.66% acetonitrile/min gradient), MART criteria to significantly reduce the instrument time required for complete MS/MS analysis of a digest separated by RP HPLC. This was confirmed by reanalyzing the set of HPLC-MALDI MS/MS data with no loss in protein identifications, despite the number of virtually executed MS/MS analyses being decreased by 57%.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".