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Record W4372332985 · doi:10.1186/s43556-023-00127-1

Targeting bromodomain-containing proteins: research advances of drug discovery

2023· review· en· W4372332985 on OpenAlexaff
Zhaoping Pan, Yuxi Zhao, Xiaoyun Wang, Xin Xie, Mingxia Liu, Kaiyao Zhang, Lian Wang, Ding Bai, Leonard J. Foster, Rui Shu, Gu He

Bibliographic record

VenueMolecular Biomedicine · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersWest China Hospital, Sichuan UniversitySichuan UniversityNational Natural Science Foundation of China
KeywordsBromodomainDrug discoveryComputational biologyDrugBiologyData scienceComputer scienceBioinformaticsPharmacologyGeneticsEpigeneticsGene

Abstract

fetched live from OpenAlex

Bromodomain (BD) is an evolutionarily conserved protein module found in 46 different BD-containing proteins (BCPs). BD acts as a specific reader for acetylated lysine residues (KAc) and serves an essential role in transcriptional regulation, chromatin remodeling, DNA damage repair, and cell proliferation. On the other hand, BCPs have been shown to be involved in the pathogenesis of a variety of diseases, including cancers, inflammation, cardiovascular diseases, and viral infections. Over the past decade, researchers have brought new therapeutic strategies to relevant diseases by inhibiting the activity or downregulating the expression of BCPs to interfere with the transcription of pathogenic genes. An increasing number of potent inhibitors and degraders of BCPs have been developed, some of which are already in clinical trials. In this paper, we provide a comprehensive review of recent advances in the study of drugs that inhibit or down-regulate BCPs, focusing on the development history, molecular structure, biological activity, interaction with BCPs and therapeutic potentials of these drugs. In addition, we discuss current challenges, issues to be addressed and future research directions for the development of BCPs inhibitors. Lessons learned from the successful or unsuccessful development experiences of these inhibitors or degraders will facilitate the further development of efficient, selective and less toxic inhibitors of BCPs and eventually achieve drug application in the clinic.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.048
GPT teacher head0.388
Teacher spread0.340 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2023
Admission routes1
Has abstractyes

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