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Record W2345555701 · doi:10.1073/pnas.1601846113

Hsp70 biases the folding pathways of client proteins

2016· article· en· W2345555701 on OpenAlexafffund
Ashok Sekhar, Rina Rosenzweig, Guillaume Bouvignies, Lewis E. Kay

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsFolding (DSP implementation)Hsp70Protein foldingComputer scienceChemistryHeat shock proteinBiochemistryEngineering

Abstract

fetched live from OpenAlex

The 70-kDa heat shock protein (Hsp70) family of chaperones bind cognate substrates to perform a variety of different processes that are integral to cellular homeostasis. Although detailed structural information is available on the chaperone, the structural features of folding competent substrates in the bound form have not been well characterized. Here we use paramagnetic relaxation enhancement (PRE) NMR spectroscopy to probe the existence of long-range interactions in one such folding competent substrate, human telomere repeat binding factor (hTRF1), which is bound to DnaK in a globally unfolded conformation. We show that DnaK binding modifies the energy landscape of the substrate by removing long-range interactions that are otherwise present in the unbound, unfolded conformation of hTRF1. Because the unfolded state of hTRF1 is only marginally populated and transiently formed, it is inaccessible to standard NMR approaches. We therefore developed a (1)H-based CEST experiment that allows measurement of PREs in sparse states, reporting on transiently sampled conformations. Our results suggest that DnaK binding can significantly bias the folding pathway of client substrates such that secondary structure forms first, followed by the development of longer-range contacts between more distal parts of the protein.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.078
GPT teacher head0.338
Teacher spread0.260 · 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

Citations108
Published2016
Admission routes2
Has abstractyes

Explore more

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