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Record W2024882722 · doi:10.1021/ja8085752

A Mutagenesis-Free Approach to Assignment of <sup>19</sup>F NMR Resonances in Biosynthetically Labeled Proteins

2009· article· en· W2024882722 on OpenAlexaff
Julianne L. Kitevski-LeBlanc, M. Sameer Al‐Abdul‐Wahid, R. Scott Prosser

Bibliographic record

VenueJournal of the American Chemical Society · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistrySide chainMutagenesisProtonStereochemistryResonance (particle physics)Fluorine-19 NMRNuclear magnetic resonance spectroscopyProton NMRChemical shiftFluorineTyrosineCrystallographyMutationBiochemistryPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Solution NMR studies of protein structure and dynamics using fluorinated amino acid probes are a valuable addition to the repertoire of existing (13)C, (15)N, and (1)H experiments. Despite the numerous advantages of the (19)F nucleus in NMR, protein studies are complicated by the dependence of resonance assignments on site-directed mutagenesis methods which are laborious and often problematic. Here we report an NMR-based route to the assignment of fluorine resonances in (13)C,(15)N-3-fluoro-l-tyrosine labeled calmodulin. The assignment begins with the correlation of the fluorine nucleus to the delta proton in the novel (13)C,(15)N-enriched probe which is achieved using a CT-HCCF-COSY experiment. Connection to the backbone is made through two additional solution NMR experiments, namely the (H(beta))C(beta)(C(gamma)C(delta))H(delta) and HNCACB. Assignments are completed using either previously published backbone chemical shift data or obtained experimentally provided uniform (13)C,(15)N labeling procedures are employed during protein expression. Additional benefits of the (13)C,(15)N-3-fluoro-l-tyrosine probe include the reduction of spectral overlap through ((13)C(19)F) CT-HSQCs, as well as the ability to monitor side chain dynamics using (19)F T(1), T(2), and the (13)C-(19)F NOE.

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 score0.015
Threshold uncertainty score0.482

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 science0.0010.000
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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations30
Published2009
Admission routes1
Has abstractyes

Explore more

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