Computational Analysis Uncovering Contrasts in G‐Quadruplex Propensity and Aptamer Enrichment in SELEX‐Derived Libraries
Bibliographic record
Abstract
G-quadruplexes (G4s) are noncanonical nucleic acid structures with biological and therapeutic significance, and they are found in many aptamer sequences. Using c-kit 1 G4 DNA as a target, systematic evolution of ligands by exponential enrichment (SELEX) has been carried out using an RNA library, resulting in G4-rich aptamers. Herein, this article investigates the relationship between predicted G4-forming potential and aptamer enrichment by analyzing high-throughput SELEX libraries using three G4 prediction tools: G4NN, G4Hunter, and QGRS Mapper. While the tools demonstrate strong internal consistency and overlap in identifying G4-prone sequences, their predictions show limited concordance with experimental abundance and enrichment trends across SELEX rounds. Only a small fraction of sequences display both high G4 scores and consistent enrichment. Experimental validation using electrophoretic mobility shift assays confirmed that strong predicted G4-forming sequences often lack strong binding activity, whereas highly enriched aptamers with strong binding activities may show weaker G4 signatures computationally. These findings suggest that current G4-prediction tools alone are insufficient for aptamer candidate selection and highlight the need for integrative evaluation strategies that combine structural prediction with empirical performance data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".