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Record W2047233829 · doi:10.1110/ps.062660907

Crystal structure of the BTB domain from the LRF/ZBTB7 transcriptional regulator

2006· article· en· W2047233829 on OpenAlexaff
P.J. Stogios, Lu Chen, Gilbert G. Privé

Bibliographic record

VenueProtein Science · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsRepressorZinc fingerTranscription factorRegulatorChemistryTranscription (linguistics)ChromatinCell biologyGeneBiologyGenetics

Abstract

fetched live from OpenAlex

BTB-zinc finger (BTB-ZF) proteins are transcription regulators with roles in development, differentiation, and oncogenesis. In these proteins, the BTB domain (also known as the POZ domain) is a protein-protein interaction motif that contains a dimerization interface, a possible oligomerization surface, and surfaces for interactions with other factors, including nuclear co-repressors and histone deacetylases. The BTB-ZF protein LRF (also known as ZBTB7, FBI-1, OCZF, and Pokemon) is a master regulator of oncogenesis, and represses the transcription of a variety of important genes, including the ARF, c-fos, and c-myc oncogenes and extracellular matrix genes. We determined the crystal structure of the BTB domain from human LRF to 2.1 A and observed the canonical BTB homodimer fold. However, novel features are apparent on the surface of the homodimer, including differences in the lateral groove and charged pocket regions. The residues that line the lateral groove have little similarity with the equivalent residues from the BCL6 BTB domain, and we show that the 17-residue BCL6 Binding Domain (BBD) from the SMRT co-repressor does not bind to the LRF BTB domain.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.207
Teacher spread0.200 · 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 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

Citations64
Published2006
Admission routes1
Has abstractyes

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