Duplexed aptamers: history, design, theory, and application to biosensing
Bibliographic record
Abstract
Nucleic acid aptamers are single stranded DNA or RNA sequences that specifically bind a cognate ligand. In addition to their widespread use as stand-alone affinity binding reagents in analytical chemistry, aptamers have been engineered into a variety of ligand-specific biosensors, termed aptasensors. One of the most common aptasensor formats is the duplexed aptamer (DA). As defined herein, DAs are aptasensors containing two nucleic acid elements coupled via Watson-Crick base pairing: (i) an aptamer sequence, which serves as a ligand-specific receptor, and (ii) an aptamer-complementary element (ACE), such as a short DNA oligonucleotide, which is designed to hybridize to the aptamer. The ACE competes with ligand binding, such that DAs generate a signal upon ligand-dependent ACE-aptamer dehybridization. DAs possess intrinsic advantages over other aptasensor designs. For example, DA biosensing designs generalize across DNA and RNA aptamers, DAs are compatible with many readout methods, and DAs are inherently tunable on the basis of nucleic acid hybridization. However, despite their utility and popularity, DAs have not been well defined in the literature, leading to confusion over the differences between DAs and other aptasensor formats. In this review, we introduce a framework for DAs based on ACEs, and use this framework to distinguish DAs from other aptasensor formats and to categorize cis- and trans-DA designs. We then explore the ligand binding dynamics and chemical properties that underpin DA systems, which fall under conformational selection and induced fit models, and which mirror classical SN1 and SN2 models of nucleophilic substitution reactions. We further review a variety of in vitro and in vivo applications of DAs in the chemical and biological sciences, including riboswitches and riboregulators. Finally, we present future directions of DAs as ligand-responsive nucleic acids. Owing to their tractability, versatility and ease of engineering, DA biosensors bear a great potential for the development of new applications and technologies in fields ranging from analytical chemistry and mechanistic modeling to medicine and synthetic biology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".