An Electrostatic Charge Partitioning Model for the Dissociation of Protein Complexes in the Gas Phase
Bibliographic record
Abstract
Electrosprayed multi-protein complexes can be dissociated by collisional activation in the gas phase. Typically, these processes follow a mechanism whereby a single subunit gets ejected with a disproportionately high amount of charge relative to its mass. This asymmetric behavior suggests that the departing subunit undergoes some degree of unfolding prior to being separated from the residual complex. These structural changes occur concomitantly with charge (proton) transfer towards the subunit that is being unraveled. Charge accumulation takes place up to the point where the subunit loses physical contact with the residual complex. This work develops a simple electrostatic model for studying the relationship between conformational changes and charge enrichment during collisional activation. Folded subunits are described as spheres that carry continuum surface charge. The unfolded chain is envisioned as random coil bead string. Simulations are guided by the principle that the system will adopt the charge configuration with the lowest potential energy for any backbone conformation. A finite-difference gradient algorithm is used to determine the charge on each subunit throughout the dissociation process. Both dimeric and tetrameric protein complexes are investigated. The model reproduces the occurrence of asymmetric charge partitioning for dissociation events that are preceded by subunit unfolding. Quantitative comparisons of experimental MS/MS data with model predictions yield estimates of the structural changes that occur during collisional activation. Our findings suggest that subunit separation can occur over a wide range of scission point structures that correspond to different degrees of unfolding.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".