MD Simulations of Peptide-Containing Electrospray Droplets: Effects of Parameter Settings on the Predicted Mechanisms of Gas Phase Ion Formation
Bibliographic record
Abstract
Electrospray ionization (ESI) mass spectrometry is widely used for interrogating peptides, proteins, and other biomolecular analytes. A growing number of laboratories use molecular dynamics (MD) simulations for uncovering ESI mechanisms by modeling the behavior of highly charged nanodroplets. The outcome of any MD simulation depends on certain assumptions and parameter settings, and it is desirable to optimize these factors by benchmarking computational data against experiments. Unfortunately, benchmarking of ESI simulations is difficult because experimentally generated gaseous ions do not generally retain any features that would reveal their formation pathway [e.g., the charged residue mechanism (CRM) or the ion evaporation mechanism (IEM)]. Here, we tackle this problem by examining the effects of various MD settings on the ESI behavior of the 9-residue peptide bradykinin in acidic aqueous droplets. Several parameters were found to significantly affect the kinetic competition between peptide IEM and CRM. By systematically probing the droplet behavior, we uncovered problems associated with certain settings, including peptide/solvent temperature imbalances, unexpected peptide deceleration during IEM, and a dependence of the ESI mechanism on the water model. We also noted different simulation outcomes for different force fields. On the basis of comprehensive tests, we propose a set of "best practice" parameter settings for MD simulations of ESI droplets. The strategies used here should be transferable to other types of droplet simulations, paving the way toward a more solid understanding of ESI mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".