EXPLORATION OF STRUCTURE-SWITCHING IN THE DESIGN OF RNA APTAMER SENSORS
Bibliographic record
Abstract
The process of ‘‘structure-switching’’ enables biomolecular switches to function as effective biosensing tools. Biomolecular switches can be activated or inactivated by binding to a specific target that triggers a precise conformational change in the biomolecules involved. Examples of aptamer-based biomolecular switches can be found in nature. Furthermore, efforts have been made in the last decade to engineer structure-switching sensors using DNA aptamers whereby, the aptamer is coupled to a signal transduction method to generate a readout signal upon target binding to the aptamer domain. Conversely, RNA aptamers have been relatively underexplored for sensor development, largely due to its susceptibility to nuclease degradation and chemical instability. Despite these shortcomings, many RNA aptamers possess superior sensing capabilities, and the abundance of RNA aptamers provides new opportunities to further advance the field. In effect, this thesis uses a structure-switching design to demonstrate the power of RNA aptamers for fluorescence-based sensor development. Herein, we demonstrate generalizable structure-switching strategies to make use of the abundance of RNA aptamers, monitor the quality control of detection and correct detection error, as well as enhance RNA aptamer sensing capability by using regulated graphene adsorption. Furthermore, our findings have expanded for secondary applications involving collaborations with other research labs. In one application, we demonstrate that entrapment of structure-switching RNA aptamers in sol-gel material confers protection against nuclease degradation and chemical instability. In another application, we further validate the use of riboswitches, or natural structure-switching RNA aptamers, as potential targets for drug discovery. Overall, these results demonstrate the capability of RNA aptamers for sensor development. We conclude with a discussion of possible areas for further inquiry, as well as future applications for the advancement of structure-switching RNA aptamers.
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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) | 0.001 | 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".