Uncovering the Pathway of Serine Octamer Magic Number Cluster Formation during Electrospray Ionization: Experiments and Simulations
Bibliographic record
Abstract
Electrospray ionization (ESI) of serine (Ser) solution generates Ser 8 H + as an abundant magic number cluster. ESI clustering of most other solutes yields nonspecific stoichiometries. It is unclear why Ser 8 H + dominates in the case of Ser, and how Ser 8 H + forms during ESI. Even the location of Ser 8 H + formation is contentious (in solution, in ESI droplets, or elsewhere). Here we unravel key aspects of the l -Ser 8 H + formation pathway. Harsh ion sampling conditions promote the collision-induced dissociation (CID) of regular ESI analytes. Unexpectedly, Ser 8 H + was seemingly resistant against CID during ion sampling, despite its extremely low tandem mass spectrometry (MS/MS) stability. This unusual behavior reveals that Ser 8 H + forms during ion sampling. We propose the following pathway: (1) Nonspecific Ser clusters are released when ESI droplets evaporate to dryness. These initial clusters cover a wide size range, from a few Ser to hundreds or thousands of monomers. (2) The clusters undergo dissociation during ion sampling, mostly via successive loss of neutral monomers. For any source activation voltage, there is a subpopulation of clusters for which this CID cascade tends to terminate at the octamer level, culminating in Ser 8 H + -dominated product distributions. Mobile proton molecular dynamics simulations were used to model the entire pathway. Ser 8 H + structures formed in these simulations were consistent with ion mobility experiments. The most compact structures resembled the model of [ Scutelnic, V. J. Am. Chem. Soc. 2018, 140, 7554–7560], with numerous intermolecular salt bridges and H-bonds. Our findings illustrate how the interplay of association and dissociation reactions across phase boundaries can culminate in magic number clusters.
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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 | 0.000 |
| 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".