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Record W1973653704 · doi:10.1021/bi050575e

Pulsed Hydrogen Exchange and Electrospray Charge-State Distribution as Complementary Probes of Protein Structure in Kinetic Experiments:  Implications for Ubiquitin Folding

2005· article· en· W1973653704 on OpenAlexaff
Jingxi Pan, Derek J. Wilson, Lars Konermann

Bibliographic record

VenueBiochemistry · 2005
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryHydrogen–deuterium exchangeElectrospray ionizationProtein foldingElectrosprayFolding (DSP implementation)Mass spectrometryKineticsNative stateProtein structureKinetic energyBiophysicsCrystallographyChromatographyBiochemistry

Abstract

fetched live from OpenAlex

The possible involvement of "hidden" kinetic intermediates in the apparent two-state folding of some proteins is currently a matter of debate. This study uses time-resolved electrospray ionization (ESI) mass spectrometry with on-line pulsed hydrogen-deuterium exchange (HDX) for monitoring the refolding of acid/methanol-denatured ubiquitin. It is demonstrated that the ESI charge-state distribution (CSD) and the extent of HDX represent nonredundant probes of the protein structure in solution. When considered in isolation, the data provided by both of these probes are consistent with a two-state behavior, involving only denatured ubiquitin D and refolded protein F. However, a careful comparison of the CSD and HDX kinetics reveals the presence of an additional species, exhibiting a CSD like the folded protein but showing non-native HDX characteristics. This kinetic intermediate, D*, is in rapid equilibrium with D, such that the overall reaction is consistent with the mechanism D <--> D* --> F. The results of this work suggest that the occurrence of transient intermediates may be more widespread than commonly thought, especially in cases where a cursory analysis indicates two-state behavior.

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 score0.005
Threshold uncertainty score0.840

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)0.0010.000

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 head0.012
GPT teacher head0.283
Teacher spread0.271 · 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

Citations45
Published2005
Admission routes1
Has abstractyes

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