Limitations of atomistic modeling to reveal ejection of proteins from charged nanodroplets
Bibliographic record
Abstract
Molecular dynamics using atomistic modeling is frequently used to extract the mechanisms of macroion release from electrosprayed droplets. However, atomistic modeling is currently feasible for only the smallest window of droplet sizes that omits the droplet history. The relevance of the observations made in this narrow window to the actual droplet evolution has not been addressed. Here, we perform a systematic study of desolvation mechanisms of poly(ethylene glycol) (PEG), protonated peptides of different compositions and protonated proteins in order to examine whether atomistic modeling can establish the extrusion mechanism of proteins from droplets. Atomistic modeling of PEG charging shows that above a critical droplet size charging occurs transiently by transfer of ions from the solvent to the macroion, while below the critical size, the capture of the ion from PEG has a lifetime sufficient for extrusion of the charged PEG from an aqueous droplet. This is the first report of the role of the droplet curvature in the charging of macroions. Modeling of the process in droplets of various sizes allow us to extrapolate the charging mechanisms in systems that cannot be modeled atomistically yet. Simulations even with highly hydrophobic peptides show that partial extrusion of a peptide from the droplet surface is rare relative to desolvation by drying-out of the protonated peptide. Differently from what has been presented in the literature we argue that atomistic simulations have not sufficiently established extrusion mechanism of proteins from droplets and their charging mechanism. Moreover, we argue that release of highly charged proteins can occur in earlier stage of a droplet's lifetime than that that is atomistically modeled. In this earlier stage, we emphasize the key role of jets emanating from a droplet at the point of charge-induced instability in the release of proteins.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".