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Record W2020712591 · doi:10.1021/ja051714i

Mapping Polypeptide Self-Recognition through <sup>1</sup>H Off-Resonance Relaxation

2005· article· en· W2020712591 on OpenAlexaff
Veronica Esposito, Rahul Das, Giuseppe Melacini

Bibliographic record

VenueJournal of the American Chemical Society · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChemistryFibrillogenesisSpinsRelaxation (psychology)PeptideSpin–lattice relaxationNuclear magnetic resonanceAnalytical Chemistry (journal)BiochemistryFibrilChromatography

Abstract

fetched live from OpenAlex

1H NMR relaxation rates provide a readily available and sensitive probe ideally suited to investigate the weak (KD approximately micromolar to millimolar range) interactions that frequently mediate polypeptide oligomerization in the early steps of amyloid fibrillogenesis. However, the measurement of transverse and longitudinal 1H relaxation rates is experimentally challenging due to J-transfer and selectivity problems in CPMG and inversion-recovery experiments, respectively. We show here that these problems are effectively circumvented by measuring nonselective off-resonance relaxation rates using an effective field tilted by 35.5 degrees . When applied to the Halpha spins of the Abeta (12-28) peptide, the proposed experiment provides a residue-resolution self-recognition map which is fully consistent with previous independent mutational studies. The method is anticipated to be widely applicable not only to the fast growing family of amyloidogenic peptides but also to the screening and mapping of protein-ligand interactions in general.

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 score0.158
Threshold uncertainty score0.375

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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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

Citations18
Published2005
Admission routes1
Has abstractyes

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