Fluorinated Ethylamines as Electrospray-Compatible Neutral pH Buffers for Native Mass Spectrometry
Bibliographic record
Abstract
Native electrospray ionization mass spectrometry (ESI-MS) has emerged as a potent tool for examining the native-like structures of macromolecular complexes. Despite its utility, the predominant “buffer” used, ammonium acetate (AmAc) with p K a values of 4.75 for acetic acid and 9.25 for ammonium, provides very little buffering capacity within the physiological pH range of 7.0–7.4. ESI-induced redox reactions alter the pH of the liquid within the ESI capillary. This can result in protein unfolding or weakening of pH-sensitive interactions. Consequently, the discovery of volatile, ESI-compatible buffers, capable of effectively maintaining pH within a physiological range, is of high importance. Here, we demonstrate that 2,2-difluoroethylamine (DFEA) and 2,2,2-trifluoroethylamine (TFEA) offer buffering capacity at physiological pH where AmAc falls short, with p K a values of 7.2 and 5.5 for the conjugate acids of DFEA and TFEA, respectively. Native ESI-MS experiments on model proteins cytochrome c and myoglobin electrosprayed with DFEA and TFEA demonstrated the preservation of noncovalent protein–ligand complexes in the gas phase. Protein stability assays and collision-induced unfolding experiments further showed that neither DFEA nor TFEA destabilized model proteins in solution or in the gas phase. Finally, we demonstrate that multisubunit protein complexes such as alcohol dehydrogenase and concanavalin A can be studied in the presence of DFEA or TFEA using native ESI-MS. Our findings establish DFEA and TFEA as new ESI-compatible neutral pH buffers that promise to bolster the use of native ESI-MS for the analysis of macromolecular complexes, particularly those sensitive to pH fluctuations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".