MétaCan
Menu
Back to cohort
Record W1984915537 · doi:10.1021/ml200147a

Exploiting the P-1 Pocket of BRCT Domains Toward a Structure Guided Inhibitor Design

2011· article· en· W1984915537 on OpenAlexaff
Ziyan Yuan, Eric A. Kumar, Stephen J. Campbell, Nicholas Y. Palermo, Smitha Kizhake, J. N. Mark Glover, Amarnath Natarajan

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2011
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Alberta
FundersNational Cancer Institute
KeywordsProtein–protein interactionPeptideDNA repairComputational biologyDNAPhosphorylationProtein structureChemistryCell biologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Breast cancer gene 1 carboxy terminus (BRCT) domains are found in a number of proteins that are important for DNA damage response (DDR). The BRCT domains bind phosphorylated proteins and these protein-protein interactions are essential for DDR and DNA repair. High affinity domain specific inhibitors are needed to facilitate the dissection of the protein-protein interactions in the DDR signaling. The BRCT domains of BRCA1 bind phosphorylated protein through a pSXXF consensus recognition motif. We identified a hydrophobic pocket at the P-1 position of the pSXXF binding site. Here we conducted a structure-guided synthesis of peptide analogs with hydrophobic functional groups at the P-1 position. Evaluation of these led to the identification of a peptide mimic 15 with a inhibitory constant (K(i)) of 40 nM for BRCT(BRCA1). Analysis of the TopBP1 and MDC1 BRCT domains suggests a similar approach is viable to design high affinity inhibitors.

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)0.001
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 score0.340
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.067
GPT teacher head0.278
Teacher spread0.210 · 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

Citations22
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueACS Medicinal Chemistry LettersSame topicComputational Drug Discovery MethodsFrench-language works237,207