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Record W4403428249 · doi:10.1038/s41467-024-52871-9

Design of a Cereblon construct for crystallographic and biophysical studies of protein degraders

2024· article· en· W4403428249 on OpenAlexfundno aff
Alena Kroupova, Valentina A. Spiteri, Zoe Rutter, Hirotake Furihata, Darren Darren, Sarath Ramachandran, Sohini Chakraborti, Kevin Haubrich, Julie Pethe, Denzel Gonzales, Andre J. Wijaya, Maria Rodriguez-Rios, Manon Sturbaut, Dylan M. Lynch, William Farnaby, Mark A. Nakasone, David Zollman, Alessio Ciulli

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceEuropean CommissionEisaiEuropean Federation of Pharmaceutical Industries and AssociationsMcGill UniversityDiamond Light SourceUniversity of Dundee
KeywordsCereblonComputational biologyUbiquitin ligaseSignal transducing adaptor proteinComputer scienceChemistryCell biologyUbiquitinBiologyBiochemistrySignal transduction

Abstract

fetched live from OpenAlex

Abstract The ubiquitin E3 ligase cereblon (CRBN) is the target of therapeutic drugs thalidomide and lenalidomide and is recruited by most targeted protein degraders (PROTACs and molecular glues) in clinical development. Biophysical and structural investigation of CRBN has been limited by current constructs that either require co-expression with the adaptor DDB1 or inadequately represent full-length protein, with high-resolution structures of degrader ternary complexes remaining rare. We present the design of CRBN midi , a construct that readily expresses from E. coli with high yields as soluble, stable protein without DDB1. We benchmark CRBN midi for wild-type functionality through a suite of biophysical techniques and solve high-resolution co-crystal structures of its binary and ternary complexes with degraders. We qualify CRBN midi as an enabling tool to accelerate structure-based discovery of the next generation of CRBN based therapeutics.

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)0.000
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 score0.276
Threshold uncertainty score0.244

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.328
Teacher spread0.291 · 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.

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

Citations38
Published2024
Admission routes1
Has abstractyes

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