Peptide separation selectivity in proteomics LC‐MS experiments: Comparison of formic and mixed formic/heptafluorobutyric acids ion‐pairing modifiers
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 R2‐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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".