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Record W2160476247 · doi:10.1042/bst20130250

HDX-MS takes centre stage at unravelling kinase dynamics

2014· review· en· W2160476247 on OpenAlexaff
Kristina Lorenzen, Tony Pawson

Bibliographic record

VenueBiochemical Society Transactions · 2014
Typereview
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
Fundersnot available
KeywordsKinaseComputational biologyBiomoleculeChemistryHydrogen–deuterium exchangeProtein dynamicsProtein kinase ADynamics (music)BiophysicsBiochemistryNanotechnologyProtein structureBiologyMaterials sciencePhysicsMass spectrometryChromatography

Abstract

fetched live from OpenAlex

In recent years, HDX-MS (hydrogen-deuterium exchange coupled to MS) on biomolecules has evolved from a niche technique to a powerful method in the investigation of protein dynamics. Protein kinases, in particular, represent an area of active study using this technique owing to their well-characterized protein structures and their relevance to diseases such as cancer, immune disorders and neurodegenerative defects. In the present review, we describe how HDX-MS has revealed important dynamic properties of protein kinases and provided insight into the mechanisms of drug binding.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.020
GPT teacher head0.285
Teacher spread0.264 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2014
Admission routes1
Has abstractyes

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