Affinity-Guided Coevolution of Aptamers for Guanine, Xanthine, Hypoxanthine, and Adenine
Bibliographic record
Abstract
The simultaneous evolution of multiple aptamers can drastically increase the speed of aptamer discovery. Most previous studies used the same concentration for different targets, leading to the dominance of the libraries by one or a few aptamers and a low success rate. To foster the best aptamers to grow independently in the sequence space, it is important to (1) use low target concentrations close to their dissociation constants and (2) stop at an early round before any sequence starts to dominate. In this study, we demonstrate this affinity-guided selection concept using the capture-SELEX method to isolate aptamers for four important purines: guanine (5 μM), xanthine (50 μM), hypoxanthine (10 μM), and adenine (10 μM). The round 9 library was split, and in round 10, the four targets were individually used to elute the binding sequences. Using thioflavin T fluorescence spectroscopy and isothermal titration calorimetry, we confirmed highly selective aptamers for xanthine, guanine, and adenine. These aptamers have K d values below 1 μM and around 100-fold selectivity against most competing analytes, and they compare favorably with existing RNA aptamers and riboswitches. A separate selection was performed using hypoxanthine alone, and no selective aptamer was achieved, even with negative selection, explaining the lack of its aptamer in our mixed selection. This affinity-guided multiplex SELEX study offers fundamental insights into aptamer selection and provides high-quality aptamers for three important purines.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".