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Record W3008322752 · doi:10.1126/sciadv.aay5064

PHOTACs enable optical control of protein degradation

2020· article· en· W3008322752 on OpenAlexfundno aff
Martin Reynders, Bryan S. Matsuura, Marleen Bérouti, Daniele Simoneschi, Antonio Marzio, Michele Pagano, Dirk Trauner

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
FundersNational Institutes of HealthNational Cancer InstituteNew York UniversityYork UniversityHoward Hughes Medical Institute
KeywordsDegradation (telecommunications)Protein degradationModular designComputer scienceControl (management)BiophysicsChemistryCell biologyBiologyBiochemistryTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

PROTACs (PROteolysis TArgeting Chimeras) are bifunctional molecules that target proteins for ubiquitylation by an E3 ligase complex and subsequent degradation by the proteasome. They have emerged as powerful tools to control the levels of specific cellular proteins. We now introduce photoswitchable PROTACs that can be activated with the spatiotemporal precision that light provides. These trifunctional molecules, which we named PHOTACs (PHOtochemically TArgeting Chimeras), consist of a ligand for an E3 ligase, a photoswitch, and a ligand for a protein of interest. We demonstrate this concept by using PHOTACs that target either BET family proteins (BRD2,3,4) or FKBP12. Our lead compounds display little or no activity in the dark but can be reversibly activated with different wavelengths of light. Our modular approach provides a method for the optical control of protein levels with photopharmacology and could lead to new types of precision therapeutics that avoid undesired systemic toxicity.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations298
Published2020
Admission routes1
Has abstractyes

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