Effects of Ammonium Bicarbonate on the Electrospray Mass Spectra of Proteins: Evidence for Bubble-Induced Unfolding
Bibliographic record
Abstract
Many protein investigations by electrospray ionization (ESI) mass spectrometry (MS) strive to ensure a "native" solvent environment, i.e., nondenaturing conditions up to the point of gas-phase ion formation. Ideally, these studies would employ a volatile pH buffer to mitigate changes in H(+) concentration that can occur during ESI. Ammonium acetate is a commonly used additive, despite its low buffering capacity at pH 7. Ammonium bicarbonate provides greatly improved pH stabilization, thus offering an interesting alternative. Surprisingly, protein analyses in bicarbonate at pH 7 tend to result in the formation of very high charge states, similar to those obtained when electrospraying unfolded proteins in a denaturing solvent. This effect has been reported previously (Sterling, H. J.; Cassou, C. A.; Susa, A. C.; Williams, E. R. Anal. Chem. 2012, 84, 3795), but its exact mechanistic origin remains unclear. ESI-mediated unfolding does not take place in acetate under otherwise identical conditions. We demonstrate that heating of protein-containing bicarbonate solutions results in extensive foaming, caused by CO2 outgassing. In contrast, acetate solutions do not generate foam. Protein denaturation caused by gas bubbles is a well-known phenomenon. Adsorption to the gas/liquid interface is accompanied by major conformational changes that allow the protein to act as a surfactant. The foaming of beer is a manifestation of this effect. Bubble formation in bicarbonate during ESI is facilitated by collisional and blackbody droplet heating. Our data imply that heat and bubbles act synergistically to cause unfolding during the electrospray process, while proteins reside in ESI droplets. Because of this effect we advise against the use of ammonium bicarbonate for native ESI-MS. Ammonium acetate represents a gentler droplet environment, despite its low buffering capacity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".