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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".