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Record W2079118456 · doi:10.1074/jbc.m107091200

Relationship between Calnexin and BiP in Suppressing Aggregation and Promoting Refolding of Protein and Glycoprotein Substrates

2001· article· en· W2079118456 on OpenAlexaff
Victoria S. Stronge, Yoshiro Saito, Yoshito Ihara, David B. Williams

Bibliographic record

VenueJournal of Biological Chemistry · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsCalnexinEndoplasmic reticulumChaperone (clinical)GlycoproteinGlycosylationChemistryBiochemistryLectinProtein foldingHsp70FoldaseCell biologyCalreticulinBiologyHeat shock protein

Abstract

fetched live from OpenAlex

Calnexin (CNX) is a membrane protein of the endoplasmic reticulum that has been defined primarily as a lectin, yet is capable of functioning as a molecular chaperone with non-glycosylated proteins in vitro. Here, we assess the relative contributions of the oligosaccharide- and polypeptide-binding sites of CNX to its in vitro chaperone functions by comparing it with the Hsp70 chaperone of the endoplasmic reticulum, BiP. Both proteins were equally effective in preventing the aggregation of non-glycosylated citrate synthase, indicating that the polypeptide-binding site of CNX is capable of functioning at a level similar to that of Hsp70. However, when confronted with glycoprotein substrates, the lectin site of CNX provided a significant advantage over BiP in suppressing aggregation. CNX also cooperated with BiP and the J domain of Sec63p in the ATP-dependent refolding of glycoprotein and non-glycosylated substrates. The lectin site of CNX was essential for refolding of the glycoprotein. These findings reinforce the function of CNX as a bona fide chaperone and illustrate how its lectin site confers advantages relative to other chaperones when confronted with glycoprotein substrates.

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)0.001
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 score0.234
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.050
GPT teacher head0.314
Teacher spread0.264 · 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

Citations66
Published2001
Admission routes1
Has abstractyes

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