Peptide Retention Standards and Hydrophobicity Indexes in Reversed-Phase High-Performance Liquid Chromatography of Peptides
Bibliographic record
Abstract
The growing utility of peptide retention prediction in proteomics would benefit from the development of a universal peptide retention standard for better alignment of chromatographic data obtained using various liquid chromatography (LC) platforms. We describe a six-peptide mixture designed for this purpose; its members cover a wide range of hydrophobicity for the most popular modes of reversed-phase peptide high-performance liquid chromatography (HPLC): C18 sorbents with trifluoroacetic/formic acid as the ion-pairing modifier and separations at pH 10. We propose a hydrophobicity index (HI) describing the concentration of organic solvent (typically acetonitrile) that yields a retention factor of 10 under isocratic elution conditions for any peptide. This measure is a fundamental characteristic of peptide-sorbent interaction, depending only on the type of sorbent and the ion-pairing modifier used. Spiking a sample with a standard peptide mixture provides a measurement of the HI values of all detected species during gradient separation. In addition to alignment of data and calibration of chromatographic runs when peptide retention prediction protocols are used, these values obtained from proteomics experiments can be utilized directly in method development for large-scale preparative separations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".