Expanded genetic alphabet increases structural and chemical diversity of six-letter DNA for high-affinity protein-targeting aptamers
Bibliographic record
Abstract
Expanding the genetic alphabet through unnatural base pairs (UBPs) enables the creation of novel biopolymers with enhanced informational and functional properties. Hydrophobic UBPs, such as Ds−Px/Pa′, exhibit high fidelity during PCR, facilitating the evolutionary engineering of UB-containing DNA aptamers (XenoAptamers) with exceptional target affinity and specificity. A series of XenoAptamers targeting dengue non-structural protein 1 (NS1), a key biomarker for dengue infection, can distinguish subtle amino acid differences among NS1 variants beyond serotypes. However, the molecular basis of this remarkable specificity and affinity remained unclear. Here, we determine cryo-EM structures of NS1−XenoAptamer complexes. Each XenoAptamer adopts a unique stable tertiary structure that precisely complements NS1’s surface, whose remarkable rigidity is key in achieving high affinity and specificity to its targets. The hydrophobic Ds base introduces these unique stable structural motifs through diverse stacking interactions. Meanwhile, the propynyl group of Pa′ inserts deeply into a hydrophobic pocket of NS1. These findings reveal that UBs expand DNA’s structural and physicochemical diversity, demonstrating their potential to create new nucleic acid modalities and opening promising avenues for diagnostics and therapeutics. Nucleic acid aptamers have emerged as promising alternatives to antibodies. Here, we show that incorporating unnatural bases enhances binding affinity by stabilizing the aptamer conformation and enabling specific engagement with hydrophobic pockets— acting like both armor and sword.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".