Screening and Identification of DNA Aptamers to Tyramine Using <i>in Vitro</i> Selection and High-Throughput Sequencing
Bibliographic record
Abstract
Aptamers are synthetic single-stranded DNA or RNA sequences that can fold into tertiary structures allowing them to interact with and bind to targets with high affinity and specificity. This paper describes the first selection and identification of DNA aptamers able to recognize the biogenic amine tyramine. To successfully isolate aptamers to this challenging small molecule target, the SELEX methodology was adapted by combining a systematic strategy to increase the selection stringency and monitor enrichment success. As the benefits of applying high-throughput sequencing (HTS) in SELEX experiments is becoming more clear, this method was employed in combination with bioinformatics analysis to evaluate the utility of the selection strategy and to uncover new potential high affinity sequences. On the basis of the presence of consensus regions (sequence families) and family similarities (clusters), 15 putative aptamers to tyramine were identified. A recently described workflow approach to perform a primary screening and characterization of the aptamer candidates by microequilibrium dialysis and by microscale thermophoresis was next leveraged. These candidate aptamers exhibited dissociation constant (Kd) values in the range of 0.2-152 μM with aptamer Tyr_10 as the most promising one followed by aptamer Tyr_14. These aptamers could be used as promising molecular recognition tools for the development of inexpensive, robust and innovative biosensor platforms for the detection of tyramine in food and beverages.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".