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Record W2035262961 · doi:10.1021/ja060817d

Relaxation Rates of Degenerate <sup>1</sup>H Transitions in Methyl Groups of Proteins as Reporters of Side-Chain Dynamics

2006· article· en· W2035262961 on OpenAlexaff
Vitali Tugarinov, Lewis E. Kay

Bibliographic record

VenueJournal of the American Chemical Society · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistrySide chainDeuteriumMethyl groupRelaxation (psychology)Molecular dynamicsDegenerate energy levelsDipoleProtein dynamicsChemical physicsComputational chemistryGroup (periodic table)PhysicsAtomic physicsOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Experiments for quantifying the amplitudes of motion of methyl-containing side chains are presented that exploit the rich network of cross-correlated spin relaxation interactions between intra-methyl dipoles in highly deuterated, selectively 13CH2D- or 13CH3-labeled proteins. In particular, the experiments measure spin relaxation rates of degenerate 1H transitions in methyl groups that, for high-molecular-weight proteins, are very simply related to methyl three-fold symmetry axis order parameters. The methodology presented is applied to studies of dynamics in a pair of systems, including the 7.5-kDa protein L and the 82-kDa enzyme malate synthase G. Good agreement between 1H- and 2H-derived measures of side-chain order are obtained on highly deuterated proteins with correlation times exceeding approximately 10 ns (correlation coefficients greater than 0.95). Although 2H- and 13C-derived measures of side-chain dynamics are still preferred, the present work underscores the potential of using 1H relaxation for semiquantitative estimates of methyl side-chain flexibility, while the high level of consistency between the different spin probes of motion establishes the reliability of the dynamics parameters.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.345

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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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

Citations68
Published2006
Admission routes1
Has abstractyes

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