Identifying G-Quadruplex-DNA-Disrupting Small Molecules
Bibliographic record
Abstract
The quest for small molecules that strongly bind to G-quadruplex-DNA (G4), so-called G4 ligands, has invigorated the G4 research field from its very inception. Massive efforts have been invested to discover or rationally design G4 ligands, evaluate their G4-interacting properties in vitro through a series of now widely accepted and routinely implemented assays, and use them as innovative chemical biology tools to interrogate cellular networks that might involve G4s. In sharp contrast, only uncoordinated efforts aimed at developing small molecules that destabilize G4s have been invested to date, even though it is now recognized that such molecular tools would have tremendous application in neurobiology as many genetic and age-related diseases are caused by an overrepresentation of G4s. Herein, we report on our efforts to develop in vitro assays to reliably identify molecules able to destabilize G4s. This workflow comprises the newly designed G4-unfold assay, adapted from the G4-helicase assay implemented with Pif1, as well as a series of biophysical and biochemical techniques classically used to study G4/ligand interactions (CD, UV–vis, PAGE, and FRET-melting), and a qPCR stop assay, adapted from a Taq -based protocol recently used to identify G4s in the genomic DNA of Schizosaccharomyces pombe . This unique, multipronged approach leads to the characterization of a phenylpyrrolocytosine (PhpC)-based G-clamp analog as a prototype of G4-disrupting small molecule whose properties are validated through many different and complementary in vitro evaluations.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".