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Record W3194473810 · doi:10.1038/s41598-021-96518-x

The chaperone HSPB1 prepares protein aggregates for resolubilization by HSP70

2021· article· en· W3194473810 on OpenAlexafffund
Conrado C. Gonçalves, Itai Sharon, T.M. Schmeing, Carlos H.I. Ramos, Jason C. Young

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchFundação de Amparo à Pesquisa do Estado de São PauloMcGill University
KeywordsChaperone (clinical)Protein aggregationHsp70LuciferaseChemistryHeat shock proteinProtein foldingCell biologyBiochemistryBiophysicsBiology

Abstract

fetched live from OpenAlex

In human cells under stress conditions, misfolded polypeptides can form potentially cytotoxic insoluble aggregates. To eliminate aggregates, the HSP70 chaperone machinery extracts and resolubilizes polypeptides for triage to refolding or degradation. Yeast and bacterial chaperones of the small heat-shock protein (sHSP) family can bind substrates at early stages of misfolding, during the aggregation process. The co-aggregated sHSPs then facilitate downstream disaggregation by HSP70. Because it is unknown whether a human sHSP has this activity, we investigated the disaggregation role of human HSPB1. HSPB1 co-aggregated with unfolded protein substrates, firefly luciferase and mammalian lactate dehydrogenase. The co-aggregates formed with HSPB1 were smaller and more regularly shaped than those formed in its absence. Importantly, co-aggregation promoted the efficient disaggregation and refolding of the substrates, led by HSP70. HSPB1 itself was also extracted during disaggregation, and its homo-oligomerization ability was not required. Therefore, we propose that a human sHSP is an integral part of the chaperone network for protein disaggregation.

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)0.002
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 score0.392
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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

Citations37
Published2021
Admission routes2
Has abstractyes

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