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Record W4224240037 · doi:10.1126/sciadv.abm7375

Efficacy and specificity of inhibitors of BCL-2 family protein interactions assessed by affinity measurements in live cells

2022· article· en· W4224240037 on OpenAlexaff
Elizabeth J. Osterlund, Nehad Hirmiz, James M. Pemberton, Adrien Nougarède, Qian Liu, Brian Leber, Qiyin Fang, David W. Andrews

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMcMaster UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsProgrammed cell deathApoptosisCell biologyProtein–protein interactionCytoplasmCellAffinitiesTarget proteinPlasma protein bindingBinding affinitiesSmall moleculeMembrane proteinChemistryCancer cellBiologyBiochemistryMembraneCancerReceptorGeneGenetics

Abstract

fetched live from OpenAlex

Cytoplasmic and membrane-bound BCL-2 family proteins regulate apoptosis, a form of programmed cell death, via dozens of binary protein interactions confounding measurement of the effects of inhibitors in live cells. In cancer, apoptosis is frequently dysregulated, and cell survival depends on antiapoptotic proteins binding to and inhibiting proapoptotic BH3 proteins. The clinical success of BH3 mimetic inhibitors of antiapoptotic proteins has spawned major efforts by the pharmaceutical industry to develop molecules with different specificities and higher affinities. Here, quantitative fast fluorescence lifetime imaging microscopy enabled comparison of BH3 mimetic drugs in trials and preclinical development by measuring drug effects on binding affinities of interacting protein pairs in live cells. Both selectivity and efficacy were assessed for 15 inhibitors of four antiapoptotic proteins for each of six BH3 protein ligands. While many drugs target the designed interaction, most also have unexpected selectivity and poor efficacy in cells.

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 score0.003
Threshold uncertainty score0.277

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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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