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Record W1984830795 · doi:10.1021/ja039587i

Probing Slow Dynamics in High Molecular Weight Proteins by Methyl-TROSY NMR Spectroscopy:  Application to a 723-Residue Enzyme

2004· article· en· W1984830795 on OpenAlexaff
Dmitry M. Korzhnev, Karin Kloiber, Voula Kanelis, Vitali Tugarinov, Lewis E. Kay

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryProtonationDeuteriumHeteronuclear single quantum coherence spectroscopyNuclear magnetic resonance spectroscopyMolecular dynamicsDipoleDispersion (optics)Analytical Chemistry (journal)MillisecondNuclear magnetic resonanceComputational chemistryStereochemistryChromatographyOrganic chemistryAtomic physicsIonPhysics

Abstract

fetched live from OpenAlex

A new CPMG-based multiple quantum relaxation dispersion experiment is presented for measuring millisecond dynamic processes at side-chain methyl positions in high molecular weight proteins. The experiment benefits from a methyl-TROSY effect in which cancellation of intramethyl dipole fields occurs, leading to methyl (13)C-(1)H correlation spectra of high sensitivity and resolution (Tugarinov, V.; Hwang, P. M.; Ollerenshaw, J. E.; Kay, L. E. J. Am. Chem. Soc. 2003, 125, 10420-10428). The utility of the methodology is illustrated with an application to a highly deuterated, methyl-protonated sample of malate synthase G, an 82 kDa enzyme consisting of a single polypeptide chain. A comparison of the sensitivity obtained using the present approach relative to existing HSQC-type (13)C single quantum dispersion experiments shows a gain of a factor of 5.4 on average, significantly increasing the range of applications for this methodology.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0010.001
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 head0.002
GPT teacher head0.221
Teacher spread0.220 · 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 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

Citations248
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicProtein Structure and DynamicsFrench-language works237,207