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Record W1987777906 · doi:10.1021/jp1055123

A Solid-State <sup>17</sup>O NMR Study of <scp>l</scp>-Tyrosine in Different Ionization States: Implications for Probing Tyrosine Side Chains in Proteins

2010· article· en· W1987777906 on OpenAlexaff
Jianfeng Zhu, Justin Y. C. Lau, Gang Wu

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsChemistryIonizationTensor (intrinsic definition)Side chainTyrosineChemical shiftIsotropySolid-state nuclear magnetic resonanceCrystallographyOxygen-17Analytical Chemistry (journal)StereochemistryNuclear magnetic resonancePhysical chemistryOrganic chemistryPhysicsIonBiochemistryGeometry

Abstract

fetched live from OpenAlex

We report experimental characterization of (17)O quadrupole coupling (QC) and chemical shift (CS) tensors for the phenolic oxygen in three l-tyrosine (l-Tyr) compounds: l-Tyr, l-Tyr.HCl, and Na(2)(l-Tyr). This is the first time that these fundamental (17)O NMR tensors are completely determined for phenolic oxygens in different ionization states. We find that, while the (17)O QC tensor changes very little upon phenol ionization, the (17)O CS tensor displays a remarkable sensitivity. In particular, the isotropic (17)O chemical shift increases by approximately 60 ppm upon phenol ionization, which is 6 times larger than the corresponding change in the isotropic (13)C chemical shift for the C(zeta) nucleus of the same phenol group. By examining the CS tensor orientation in the molecular frame of reference, we discover a "cross-over" effect between delta(11) and delta(22) components for both (17)O and (13)C CS tensors. We demonstrate that the knowledge of such "cross-over" effects is crucial for understanding the relationship between the observed CS tensor components and chemical bonding. Our results suggest that solid-state (17)O NMR can potentially be used to probe the ionization state of tyrosine side chains in proteins.

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 score0.011
Threshold uncertainty score0.669

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 integrity0.0000.001
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 head0.014
GPT teacher head0.302
Teacher spread0.288 · 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

Citations42
Published2010
Admission routes1
Has abstractyes

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