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Record W2278671746

Targeted approach towards inhibition of telomere-hnRNP A1 interaction

2007· article· en· W2278671746 on OpenAlexaff
Laurent Bélec, Richard Marcellus, Xavier Billot, Stéphane Branchaud, Sylvie Charron, Kenza Daira, Lionel Dumas, Gaétan Gagnon, Gerson Gonzalez, Abdelkrim Khadir, Sandra Naranjo, Daniel Rabouin, Mohammad Askari, Mukta Ullah, Joseph D. Schrag, Yunge Li, Mirosław Cygler, Michael Lawless, Gordon C. Shore, Pierre Beauparlant

Bibliographic record

VenueMolecular Cancer Therapeutics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Nucleic Acid Chemistry
Canadian institutionsNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsTelomereTelomeraseSmall moleculeDNAOligonucleotideBiologyIn vitroMolecular biologyG-quadruplexComputational biologyBiochemistryChemistryGene
DOInot available

Abstract

fetched live from OpenAlex

A207 The heterogeneous nuclear ribonucleoparticule (hnRNP) A1 and A2 proteins associate with telomere ends (the cap structure), stimulate telomerase activity and are required for the viability of transformed human cells, irrespective of the status of telomerase expression or the length of the double-stranded telomeric repeat. We describe here our screening strategy for identifying small molecules capable of interfering with telomere capping by A1 and A2. Such small molecules are potentially promising novel anti-cancer agents predicted to be acutely cytotoxic to cancer cells, but innocuous to untransformed cells. In our first attempt, libraries were screened directly in a protein/DNA dissociation assay using a truncated version of the A1 protein: UP1 and a short telomeric DNA oligo. After examining 60,000 preselected compounds, no validated hits were identified. Only promiscuous inhibitors with the tendency to form aggregates in solution showed activity in vitro. A more selective screening approach was then undertaken. A large collection of compounds from different libraries was filtered to remove compound with undesired physicochemical properties and functional groups. This resulted in a database containing approximately 2 million compounds. Based on published X-ray crystallography structures, we chose to target the binding pocket that contacts the nucleotides TAG within the telomeric sequence TTAGGG. We resolved by X-ray diffraction the structure of isolated TAG oligonucleotide bound to UP1 and found this ligand exhibited the same network of interactions within the pocket as the full length telomeric repeat. We then designed a series of 2D and 3D pharmacophores based both on the protein binding site and the TAG ligand. The collection and sub-sets thereof were screened against these pharmacophores and several compounds were identified. The selected compounds were then docked in silico into the binding site and ranked using docking scores. The best compounds from each of these screens were obtained and screened. The present screening workflow consists of an initial disruption assay (to determine which compounds inhibit the targeted interaction), solubility assessment by nephelometry (to exclude insoluble compounds), binding studies to DNA and unrelated protein targets (to eliminate undesired binding activities), UP1 binding assessment using Surface Plasmon Resonance, cytotoxicity testing followed by in vivo target modulation. SAR studies were initiated on five compound classes that showed activity. A correlation between in vitro and in vivo activity was revealed for two closely related compound classes. Synthesis of analogues from these two classes is presently underway to increase potency. The virtual-HTS hybrid approach described here was critical for obtaining active and optimizable scaffolds, with the potential to develop potent inhibitors of telomere capping 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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
Published2007
Admission routes1
Has abstractyes

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