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Record W2789808439 · doi:10.1002/pep2.24048

Peptide therapeutics that directly target transcription factors

2018· article· en· W2789808439 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePeptide Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTranscription factorTranscription (linguistics)Computational biologyCell biologySp3 transcription factorBiologyE-boxGeneral transcription factorTAF2PromoterGeneGene expressionGeneticsEnhancer

Abstract

fetched live from OpenAlex

Abstract Transcription factors regulate gene expression in cells and control cellular development, function, and death. Dysregulation of transcription factors is often associated with disease, including cancer. As such, transcription factors are attractive targets for design of therapeutics against disease. Transcription factors function using protein‐protein and protein‐DNA interactions that occur over relatively large surface areas: this lack of a small and defined “ligand binding site” has proven to be challenging to target with small molecules. Peptide therapeutics, therefore, provide an alternate approach toward design of inhibitory agents. Transcription factors are conveniently modular by design: just the small domain that is responsible for the transcription factor's DNA binding or a protein‐protein interaction or another function, can serve as the basis for novel peptide therapeutics. In this review, examples of peptides that directly interfere with transcription factors will be discussed.

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.

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)0.000
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 score0.046
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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