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Record W2082068205 · doi:10.1073/pnas.1405011111

Measuring hydrogen exchange rates in invisible protein excited states

2014· article· en· W2082068205 on OpenAlexafffund
Dong Long, Guillaume Bouvignies

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsSickKids FoundationHospital for Sick ChildrenCanada Research ChairsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsChemistryExcited stateNative stateProtein foldingSolventHydrogenHydrogen bondChemical shiftCrystallographyAmideChemical physicsPhysical chemistryMoleculeAtomic physicsPhysicsOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

Hydrogen exchange rates have become a valuable probe for studying the relationship between dynamics and structure and for dissecting the mechanism by which proteins fold to their native conformation. Typically measured rates correspond to averages over all protein states from which hydrogen exchange can occur. Here we describe a new NMR experiment based on chemical exchange saturation transfer that provides an avenue for obtaining uncontaminated, per-residue amide hydrogen exchange rates for interconverting native and invisible states so long as they can be separated on the basis of distinct (15)N chemical shifts. The approach is applied to the folding reaction of the Fyn SH3 domain that exchanges between a highly populated, NMR-visible native state and a conformationally excited, NMR-invisible state, corresponding to the unfolded ensemble. Excellent agreement between experimentally derived hydrogen exchange rates of the excited state at a pair of pHs is obtained, taking into account the expected dependence of exchange on pH. Extracted rates for the unfolded ensemble have been used to test hydrogen exchange predictions based on the primary protein sequence that are used in many analyses of solvent exchange rates, with a Pearson correlation coefficient of 0.84 obtained.

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 score0.051
Threshold uncertainty score0.204

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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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

Citations49
Published2014
Admission routes2
Has abstractyes

Explore more

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