Molecular Dynamics Simulations on Gas-Phase Proteins with Mobile Protons: Inclusion of All-Atom Charge Solvation
Bibliographic record
Abstract
Molecular dynamics (MD) simulations have become a key tool for examining the properties of electrosprayed protein ions. Traditional force fields employ static charges on titratable sites, whereas in reality, protons are highly mobile in gas-phase proteins. Earlier studies tackled this problem by adjusting charge patterns during MD runs. Within those algorithms, proton redistribution was subject to energy minimization, taking into account electrostatic and proton affinity contributions. However, those earlier approaches described (de)protonated moieties as point charges, neglecting charge solvation, which is highly prevalent in the gas phase. Here, we describe a mobile proton algorithm that considers the electrostatic contributions from all atoms, such that charge solvation is explicitly included. MD runs were broken down into 50 ps fixed-charge segments. After each segment, the electrostatics was reanalyzed and protons were redistributed. Challenges associated with computational cost were overcome by devising a streamlined method for electrostatic calculations. Avidin (a 504-residue protein complex) maintained a nativelike fold over 200 ns. Proton transfer and side chain rearrangements produced extensive salt bridge networks at the protein surface. The mobile proton technique introduced here should pave the way toward future studies on protein folding, unfolding, collapse, and subunit dissociation in the gas phase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".