Preventing <i>N</i>‐ and <i>O</i>‐formylation of proteins when incubated in concentrated formic acid
Bibliographic record
Abstract
Concentrated formic acid is among the most effective solvents for protein solubilization. Unfortunately, this acid also presents a risk of inducing chemical modifications thereby limiting its use in proteomics. Previous reports have supported the esterification of serine and threonine residues (O-formylation) for peptides incubated in formic acid. However as shown here, exposure of histone H4 to 80% formic (1 h, 20(o) C) induces N-formylation of two independent lysine residues. Furthermore, incubating a mixture of Escherichia coli proteins in formic acid demonstrates a clear preference toward lysine modification over reactions at serine/threonine. N-formylation accounts for 84% of the 225 uniquely identified formylation sites. To prevent formylation, we provide a detailed investigation of reaction conditions (temperature, time, acid concentration) that define the parameters permitting the use of concentrated formic acid in a proteomics workflow for MS characterization. Proteins can be maintained in 80% formic acid for extended periods (24 h) without inducing modification, so long as the temperature is maintained at or below -20(o) C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".