Manipulation of a DNA aptamer–protein binding site through arylation of internal guanine residues
Bibliographic record
Abstract
Chemically modified aptamers have the opportunity to increase aptamer target binding affinity and provide structure-activity relationships to enhance our understanding of molecular target recognition by the aptamer fold. In the current study, 8-aryl-2'-deoxyguanosine nucleobases have been inserted into the G-tetrad and central TGT loop of the thrombin binding aptamer (TBA) to determine their impact on antiparallel G-quadruplex (GQ) folding and thrombin binding affinity. The aryl groups attached to the dG nucleobase vary greatly in aryl ring size and impact on GQ stability (∼20 °C change in GQ thermal melting (Tm) values) and thrombin binding affinity (17-fold variation in dissociation constant (Kd)). At G8 of the central TGT loop that is distal from the aptamer recognition site, the probes producing the most stable GQ structure exhibited the strongest thrombin binding affinity. However, within the G-tetrad, changes to the electron density of the dG component within the modified nucleobase can diminish thrombin binding affinity. Detailed molecular dynamics (MD) simulations on the modified TBA (mTBA) and mTBA-protein complexes demonstrate how the internal 8-aryl-dG modification can manipulate the interactions between the DNA nucleobases and the amino acid residues of thrombin. These results highlight the potential of internal fluorescent nuclobase analogs (FBAs) to broaden design options for aptasensor development.
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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 | 0.000 |
| 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) | 0.001 | 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 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".