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Record W1854077804 · doi:10.1007/s10858-015-9994-8

Cross-correlated relaxation measurements under adiabatic sweeps: determination of local order in proteins

2015· article· en· W1854077804 on OpenAlexfundno aff
Pavel Kadeřávek, Sarina Grutsch, Nicola Salvi, Martin Tollinger, Lukáš Žı́dek, Geoffrey Bodenhausen, Fabien Ferrage

Bibliographic record

VenueJournal of Biomolecular NMR · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsnot available
FundersFP7 Research Potential of Convergence RegionsEuropean Research CouncilAustrian Science FundSCIEX
KeywordsAdiabatic processChemistryTransverse planeCross-correlationComputational physicsNuclear magnetic resonancePhysicsQuantum mechanicsStatistics

Abstract

fetched live from OpenAlex

Adiabatically swept pulses were originally designed for the purpose of broadband spin inversion. Later, unexpected advantages of their utilization were also found in other applications, such as refocusing to excite spin echoes, studies of chemical exchange or fragment-based drug design. Here, we present new experiments to characterize fast (ps-ns) protein dynamics, which benefit from little-known properties of adiabatic pulses. We developed a strategy for measuring cross-correlated cross-relaxation (CCCR) rates during adiabatic pulses. This experiment provides a linear combination of longitudinal and transverse CCCR rates, which is offset-independent across a typical amide (15)N spectrum. The pulse sequence can be recast to provide accurate transverse CCCR rates weighted by the populations of exchanging states. Sensitivity can be improved in systems in slow exchange. Finally, the experiments can be easily modified to yield residue-specific correlation times. The average correlation time of motions can be determined with a single experiment while at least two different experiments had to be recorded until now.

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)0.001
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 score0.145
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.311
Teacher spread0.285 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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