Mechanism of Protein Supercharging by Sulfolane and <i>m</i>-Nitrobenzyl Alcohol: Molecular Dynamics Simulations of the Electrospray Process
Bibliographic record
Abstract
Electrospray ionization (ESI) allows the production of intact gas-phase ions from proteins in solution. Nondenaturing solvent conditions usually culminate in low ESI charge states. However, many mass spectrometric applications benefit from protein ions that are more highly charged. One way to boost protein charge is the addition of supercharging agents (SCAs) such as sulfolane or m-nitrobenzyl alcohol (m-NBA) to the aqueous solution. The supercharging mechanism remains controversial. We use molecular dynamics (MD) simulations to examine how SCAs affect the behavior of ESI nanodroplets. Simulations were conducted on myoglobin in water, water/sulfolane, and water/m-NBA. Na(+) ions served as surrogate charge carriers instead of H(+). We focus on conditions where the protein initially adopts its native conformation. MD-generated charge states show remarkable agreement with experimental data. Droplet shrinkage is accompanied by Na(+) ejection, consistent with the ion evaporation model (IEM). The droplets segregate into an outer SCA shell and an aqueous core. This core harbors protein and Na(+). Unfavorable SCA solvation restricts Na(+) access to the droplet surface, thereby impeding IEM ejection. Rapid water loss causes SCA enrichment, ultimately forcing all remaining Na(+) to bind the protein. IEM ejection is no longer feasible after this point, such that the protein becomes supercharged by Na(+) trapping. SCA-free droplets produce lower charge states because the aqueous environment ensures a higher IEM efficiency. For all scenarios examined here, proteins are released via solvent evaporation to dryness, as envisioned by the charged residue model. Our data provide the first atomistic view of the supercharging mechanism.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".