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Record W2145672779 · doi:10.1139/v04-154

Using ellipsoids to model charge distributions in gas phase protein complex ion dissociation

2004· article· en· W2145672779 on OpenAlexfundvenueno aff
Susan A. Csiszar, Mark Thachuk

Bibliographic record

VenueCanadian Journal of Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryDissociation (chemistry)IonAsymmetryEllipsoidElectrostaticsAtomic physicsSurface chargeMolecular physicsChemical physicsAnalytical Chemistry (journal)Physical chemistryPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Gas phase protein complex dissociation was modelled using ellipsoids on which discrete charges were placed in randomly chosen charge sites. A number of sizes, shapes, orientations, and types of ellipsoids were considered. For each case, charge transfer parameters and electrostatic energies were calculated as a function of the fractional surface area. It was found that to within 10–15%, the charge on product ions after dissociation is distributed according to their fractional surface area. This can imply, for example, that in experiments measuring the dissociation of homodimers, charge asymmetries of greater than 10–15% in the product ions signal that one of the ions has greatly increased its surface area, such as would occur with unfolding. This assumes that proton transfer occurs on a timescale fast enough that the dissociation products adopt the minimum electrostatic energy configuration. Calculation of this energy shows that it is minimized when one or more of the monomers is in an extended conformation.Key words: protein complex dissociation, ellipsoid charge model, charge asymmetry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.035
GPT teacher head0.298
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2004
Admission routes2
Has abstractyes

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