Selection of allosteric dnazymes that can sense phenylalanine by expression-SELEX
Bibliographic record
Abstract
Aptamers are ligand-binding RNA or DNA molecules and have been widely examined as biosensors, diagnostic tools, and therapeutic agents. The application of aptamers as biosensors commonly requires an expression platform to produce a signal to report the aptamer-ligand binding event. Traditionally, aptamer selection and expression platform integration are two independent steps and the aptamer selection requires the immobilization of either the aptamer or the ligand. These drawbacks can be easily overcome through the selection of allosteric DNAzymes (aptazymes). Herein, we used the technique of Expression-SELEX developed in our laboratory to select for aptazymes that can be specifically activated by low concentrations of l-phenylalanine. We chose a previous DNA-cleaving DNAzyme known as II-R1 as the expression platform for its low cleavage rate and used stringent selection conditions to drive the selection of high-performance aptazyme candidates. Three aptazymes were chosen for detailed characterization and these DNAzymes were found to exhibit a dissociation constant for l-phenylalanine as low as 4.8 μM, a catalytic rate constant improvement as high as 20 000-fold in the presence of l-phenylalanine, and the ability to discriminate against closely related l-phenylalanine analogs including d-phenylalanine. This work has established the Expression-SELEX as an effective SELEX method to enrich high-quality ligand-responsive aptazymes.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".