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Record W4405000417 · doi:10.26434/chemrxiv-2024-lng9q

Exploring the Ligandability of 53BP1 Through Fragment-Based Approaches

2024· preprint· en· W4405000417 on OpenAlexafffund
Beatrice Chiew, Menachem J. Gunzburg, Caroline A. Foley, Hong Zeng, A. Dong, Peter J. Brown, Juliana The, Jacqueline L. Noris, Stephanie H. Cholensky, Biswaranjan Mohanty, M.J. Scanlon, Stephen J. Headey, B.C. Doak, Lindsey I. James

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicCancer Mechanisms and Therapy
Canadian institutionsUniversity of Toronto
FundersStructural Genomics ConsortiumNational Institutes of HealthEshelman Institute for Innovation, University of North Carolina at Chapel HillOntario Genomics Institute
KeywordsHistoneDNAComputational biologyBiologyGeneticsChemistryCancer researchMolecular biologyCell biology

Abstract

fetched live from OpenAlex

53BP1 is a DNA damage response protein recruited to sites of double strand breaks through recognition of dimethylated lysine on histone 4 by its tandem Tudor domains. Like 53BP1, BRCA-1 plays a role in the regulation of DNA repair pathways, and BRCA-1 mutations have been strongly linked to breast and ovarian cancer. Interestingly, mice null for 53BP1 and BRCA-1 genes display minimal tumor formation, suggesting that the effects of deleterious BRCA-1 mutations could be prevented with potent 53BP1 small molecule antagonists. Herein, we describe a fragment screen that was used to identify compounds that bind to the 53BP1 Tudor domain and a chemoinformatic workflow to select near-neighbour analogues and establish Structure Activity Relationships for these binders. The marked affinity improvements of the analogues over their parent fragments highlights the developability of these series and the utility of this approach in discovering novel hit compounds for 53BP1 and other methyl-lysine reader proteins.

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.221
GPT teacher head0.308
Teacher spread0.087 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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