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Record W2320420686 · doi:10.1021/ct300906a

A Charge Moving Algorithm for Molecular Dynamics Simulations of Gas-Phase Proteins

2013· article· en· W2320420686 on OpenAlexafffund
Sarah K. Fegan, Mark Thachuk

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of British Columbia
FundersWestern Canada Research Grid
KeywordsMolecular dynamicsRadius of gyrationCharge (physics)Monte Carlo methodCoulombChemical physicsForce field (fiction)Statistical physicsElectrostaticsDissociation (chemistry)PhysicsChemistryComputational chemistryPhysical chemistryElectronPolymerQuantum mechanics

Abstract

fetched live from OpenAlex

A method for moving charges in a coarse-grained simulation of gas-phase proteins is presented which uses a Monte Carlo approach to move charges between charge sites. The method is used to study the role of charge movement in the dissociation mechanism of protein complexes in order to better understand experimentally observed mass spectra from CID studies. The charge hopping process is analyzed using energy distributions and a pair correlation plot. Hopping rates, charge distributions, and structural parameters (radius of gyration and RMSD) are also calculated. The importance of charge movement for the unfolding of protein complexes is demonstrated. The algorithm is implemented in the GROMACS molecular dynamics software package. In this study, transthyretin (TTR) tetramer is used with the MARTINI force field as a model system, and comparisons to experiments are made. The hopping and unfolding are found to be controlled by the Coulomb repulsion among the charges in the complex.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.285
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations51
Published2013
Admission routes2
Has abstractyes

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