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Record W2040268937 · doi:10.1158/1538-7445.am2013-5139

Abstract 5139: Molecular docking exploration of potential RET tyrosine kinase inhibitors at non ATP-binding sites.

2013· article· en· W2040268937 on OpenAlexaff
Adrian C. Nicolescu, Taranjit S. Gujral, Jordan D.S. Zelt, Lois M. Mulligan

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsQueen's University
Fundersnot available
KeywordsReceptor tyrosine kinaseProto-Oncogene Proteins c-retGlial cell line-derived neurotrophic factorBiologyTyrosine kinaseProtein kinase domainROR1Cancer researchCell biologyPlatelet-derived growth factor receptorBiochemistrySignal transductionReceptorNeurotrophic factors

Abstract

fetched live from OpenAlex

Abstract RET (REarranged during Transfection) tyrosine kinase receptor is a transmembrane protein required for the development of neural-crest-derived cells, the urogenital system, and the central and peripheral nervous systems, notably the enteric nervous system. The RET protein has an extracellular domain with a cysteine-rich region and four cadherin-like domains, a transmembrane domain, and an intracellular tyrosine kinase domain, required for RET phosphorylation and downstream signaling. The structure of the RET kinase shares with other tyrosine kinases many conserved functional motifs and regulatory residues that are important for the kinase enzyme function. RET activation requires binding of a glial cell-line-derived neurotrophic factor (GDNF) and a co-receptor of the GDNF family receptors α. Germline mutations of RET, leading to uncontrolled activation, are associated with thyroid cancer, and recently with colorectal and lung cancers, and chronic myelomonocytic leukemia. RET mutations that result in decreased receptor function have been linked to developmental defects, such as Hirschsprung disease and kidney anomalies. The design of receptor tyrosine kinase (RTK) inhibitors has traditionally targeted the enzymes’ highly conserved ATP binding pocket. This approach resulted in the discovery of potent small molecule inhibitors, but with relatively low selectivity. To date there are no RET-specific inhibitors available for therapy, although few small molecule inhibitors are undergoing clinical evaluations as potential RET inhibitors. Since there are important critical regions within the RET molecule (e.g., the substrate binding pocket and activation loop) that contain unique amino acids and structural features, we used the method of molecular docking to virtually screen a diverse library of compounds that potentially target non-ATP binding sites of RET. Known ligands of RET were used to test the success of docking before the library of compounds was screened, docking solutions were inspected and ranked, and best compounds selected for novel structural features and future testing. Multiple comparisons with other known structures of RTK-ligand complexes have been performed in order to identify molecular features that would characterize the discovery of potential RET-specific inhibitors. Citation Format: Adrian C. Nicolescu, Taranjit S. Gujral, Jordan DS Zelt, Lois M. Mulligan. Molecular docking exploration of potential RET tyrosine kinase inhibitors at non ATP-binding sites. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5139. doi:10.1158/1538-7445.AM2013-5139 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.025

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.053
GPT teacher head0.357
Teacher spread0.304 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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