MétaCan
Menu
Back to cohort
Record W2894149277 · doi:10.1515/hsz-2018-0251

A novel method for site-specific chemical SUMOylation: SUMOylation of Hsp90 modulates co-chaperone binding <i>in vitro</i>

2018· article· en· W2894149277 on OpenAlexafffund
Annemarie Wolmarans, Allison Kwantes, Paul LaPointe

Bibliographic record

VenueBiological Chemistry · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsThe King's UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSUMO proteinHsp90ChemistryIn vitroChaperone (clinical)Cell biologyBinding siteBiophysicsBiochemistryBiologyHeat shock proteinGeneMedicine

Abstract

fetched live from OpenAlex

SUMO is covalently attached to lysine side chains in target proteins by the action of a cascade of E1, E2, and E3 ligases. Unlike ubiquitin, SUMO does not target proteins for degradation but rather plays a regulatory role in activating target proteins or directing them to multiprotein complexes. Isolating SUMOylated proteins from native sources is challenging because of the low stoichiometry of SUMOylation that occurs for any given target protein in cells. Here we report a novel strategy to couple SUMO to the site of a target lysine for the purpose of in vitro study. Introduction of a single cysteine after the C terminal diglycine motif and a cysteine in place of a target lysine in a substrate protein allows for efficient and specific crosslinking of SUMO using a homo-bifunctional maleimide crosslinker. We demonstrate that SUMO can be crosslinked in this manner to amino acid position 178 in the dimeric molecular chaperone, Hsp90. Chemically SUMOylated Hsp90 has very similar ATPase activity compared to unmodified Hsp90 but displays preferential co-chaperone binding in vivo. Our novel strategy can easily be applied to other SUMOylated or ubiquitinated target protein in vitro.

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 score0.008
Threshold uncertainty score0.770

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 integrity0.0010.000
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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBiological ChemistrySame topicHeat shock proteins researchFrench-language works237,207