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Fluorescent biosensors for probing CDK4/cyclin D activity and developing non‐ATP pocket Inhibitors for melanoma, lung cancer and lymphoma (972.2)

2014· article· en· W1619197792 on OpenAlexfundno aff
May C. Morris, Camille Prével, Morgan Pellerano, Thi Nhu Ngoc Van

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsnot available
FundersDivision of Arctic SciencesCNIB
KeywordsKinaseCancer researchBiosensorAnaplastic lymphoma kinaseMelanomaChemistryLung cancerBiologyBiochemistryMedicineOncology

Abstract

fetched live from OpenAlex

CDK4/cyclin D kinase constitutes an attractive pharmacological target in lung cancer, melanoma and lymphoma, associated with mutation or amplification of CDK4, cyclin D or p16INK4a, but efforts to develop tools for detection of this kinase in its native environment, as well as selective inhibitors for therapeutic purposes have remained limited. To this aim we have engineered a fluorescent polypeptide biosensor that reports on CDK4/cyclin D activity in a sensitive and continuous fashion in vitro, in living cells and in biopsies and which allows to monitor response to therapeutics in animal tumour models. We have further designed two biosensors which have been applied to identify competitors of essential protein/protein interfaces between CDK4 and Cyclin D, and allosteric inhibitors that perturb the conformational dynamics of CDK4, respectively, by high throughput screening. These studies highlight the importance of fluorescent biosensors for fundamental research, biomedical developments and drug discovery programmes, providing novel and sensitive approaches to monitor cancer‐associated alterations in protein kinase activities and develop non‐ATP pocket inhibitors. Grant Funding Source : Grants to MCM “Chercheuse d’Avenir” Région Languedoc‐Roussillon, ARC and INCA

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.606

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 studies0.0010.000
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 head0.016
GPT teacher head0.291
Teacher spread0.275 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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