Peptide separation selectivity in proteomics LC‐MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion‐pairing modifiers
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Bibliographic record
Abstract
Abstract Separation selectivity and detection sensitivity of reversed‐phase high‐performance liquid chromatography with tandem mass spectrometry analyses were compared for formic (0.1%) and formic/heptafluorobutyric (0.1%/0.005%) acid based eluents using a proteomic data set of ∼12 000 paired peptides. The addition of a small amount of hydrophobic heptafluorobutyric acid ion‐pairing modifier increased peptide retention by up to 10% acetonitrile depending on peptide charge, size, and hydrophobicity. Retention increase was greatest for peptides that were short, highly charged, and hydrophilic. There was an ∼3.75‐fold reduction in MS signal observed across the whole population of peptides following the addition of heptafluorobutyric acid. This resulted in ∼36% and ∼21% reduction of detected proteins and unique peptides for the whole cell lysate digests, respectively. We also confirmed that the separation selectivity of the formic/heptafluorobutyric acid system was very similar to the commonly used conditions of 0.1% trifluoroacetic acid, and developed a new version of the Sequence‐Specific Retention calculator model for the formic/heptafluorobutyric acid system showing the same ∼0.98 R 2 ‐value accuracy as the Sequence‐Specific Retention calculator formic acid model. In silico simulation of peptide distribution in separation space showed that the addition of 0.005% heptafluorobutyric acid to the 0.1% formic acid system increased potential proteome coverage by ∼11% of detectable species (tryptic peptides ≥ four amino acids).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it