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Disorder in Protein Switches

2015· article· en· W1090154364 on OpenAlexaff
Jörg Gsponer

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Structure and Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAllosteric regulationChemistryFunction (biology)Steric effectsMolecular recognitionProtein domainComputational biologyProtein–protein interactionBiophysicsIntrinsically disordered proteinsProtein structureCell biologyBiochemistryBiologyStereochemistryGeneMoleculeEnzyme

Abstract

fetched live from OpenAlex

Interactions of intrinsically disordered (ID) protein segments with partner proteins are often mediated by molecular recognition features (MoRFs). MoRFs in ID segments are not only used in interactions in trans but also in cis, i.e., they can mediate interactions between an ID segment and a domain that are both part of the same polypeptide chain. Such interactions in cis can switch the function of a protein domain off by steric or allosteric inhibition of substrate or binding partner access to catalytic sites or binding surfaces, respectively. This phenomenon is called autoinhibition. We demonstrate that most cis‐regulatory elements in proteins are ID and that the MoRFs in these ID autoinhibitory regions are highly conserved. We present results from molecular dynamics simulations that reveal the mechanism by which phosphorylation of MoRFs can relieve autoinhibition and introduce the first predictor that identifies ID autoinhibitory regions in proteins from sequence information only.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.237
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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