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Record W2738817380 · doi:10.1021/acs.analchem.7b01941

Advances in Modeling the Stability of Noncovalent Complexes in Charged Droplets with Applications in Electrospray Ionization-MS Experiments

2017· article· en· W2738817380 on OpenAlexafffund
Styliani Consta, Mahmoud Sharawy, Myong In Oh, Anatoly Malevanets

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryElectrospray ionizationNon-covalent interactionsElectrosprayDissociation (chemistry)Mass spectrometryDissociation constantChemical physicsIonizationAqueous solutionComputational chemistryNanotechnologyMoleculePhysical chemistryChromatographyIonOrganic chemistryHydrogen bond

Abstract

fetched live from OpenAlex

Electrospray ionization mass spectrometry is used extensively to measure the equilibrium constant of noncovalent complexes. In this Perspective, we attempt to present an accessible introduction to computational methodologies that can be applied to determine the stability of weak noncovalent complexes in their journey from bulk solution into the gaseous state. We demonstrate the usage of the methods on two typical examples of noncovalent complexes drawn from a broad class of nucleic acids and transient protein complexes found in aqueous droplets. We conclude that this new emerging direction in the use of simulations can lead to estimates of equilibrium constant corrections due to complex dissociation in the carrier droplet and finding of agents that may stabilize the protein interfaces.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.312
Teacher spread0.290 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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