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Record W3197561900 · doi:10.3390/molecules26185460

Molecular Determinant of DIDS Analogs Targeting RAD51 Activity

2021· article· en· W3197561900 on OpenAlexafffund
Denis Velic, Alexandre Demeyer, Thibaut Peterlini, Houda Benhelli‐Mokrani, Monique Mathé‐Allainmat, Jean‐Yves Masson, Fabrice Fleury

Bibliographic record

VenueMolecules · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchConseil Régional des Pays de la Loire
KeywordsRAD51DIDSHomologous recombinationChemistryBiochemistryCell biologyBiologyDNA

Abstract

fetched live from OpenAlex

RAD51 is the central protein in DNA repair by homologous recombination (HR), involved in several steps of this process. It is shown that overexpression of the RAD51 protein is correlated with increased survival of cancer cells to cancer treatments. For the past decade, RAD51 overexpression-mediated resistance has justified the development of targeted inhibitors. One of the first molecules described to inhibit RAD51 was the 4,4'-diisothiocyanato-stilbene-2,2'-disulfonic acid (DIDS) molecule. This small molecule is effective in inhibiting different functions of RAD51, however its mode of action and the chemical functions involved in this inhibition have not been identified. In this work, we used several commercial molecules derived from DIDS to characterize the structural determinants involved in modulating the activity of RAD51. By combining biochemical and biophysical approaches, we have shown that DIDS and two analogs were able to inhibit the binding of RAD51 to ssDNA and prevent the formation of D-loop by RAD51. Both isothiocyanate substituents of DIDS appear to be essential in the inhibition of RAD51. These results open the way to the synthesis of new molecules derived from DIDS that should be greater modulators of RAD51 and more efficient for HR inhibition.

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 score0.013
Threshold uncertainty score0.704

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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

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