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Record W4410716975 · doi:10.1007/s10858-025-00469-8

Estimating cross-relaxation rates between methyl and neighboring labile proton spins in high molecular weight proteins

2025· article· en· W4410716975 on OpenAlexfundno aff
Vitali Tugarinov, G. Marius Clore

Bibliographic record

VenueJournal of Biomolecular NMR · 2025
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthUniversity of Toronto
KeywordsChemistrySpinsProtonRelaxation (psychology)Methyl groupMagnetization transferSaturation (graph theory)Analytical Chemistry (journal)CrystallographyChemical physicsChromatographyOrganic chemistryGroup (periodic table)

Abstract

fetched live from OpenAlex

Abstract We show that water saturation leads to deleterious losses in sensitivity of methyl signals in selectively methyl-[ 13 CH 3 ]-labeled protein samples of high molecular weight proteins dissolved in H 2 O. These losses arise from efficient cross-relaxation between methyl protons and proximal labile protons in the protein structure. A phenomenological model for analysis of methyl intensity decay profiles that involves exchange saturation transfer of magnetization from localized proton spins of water to various labile groups in the protein structure that, in turn, efficiently cross-relax with protons of methyl groups, is described. Analysis of methyl intensity decay profiles with this model allows cross-relaxation rates (σ) between methyl and labile protons to be determined and permits identification of methyl sites in close proximity to labile groups in the protein structure.

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 score0.182
Threshold uncertainty score0.612

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 head0.008
GPT teacher head0.324
Teacher spread0.316 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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