Truncations and in silico Docking to Enhance the Analytical Response of Aptamer-Based Biosensors
Bibliographic record
Abstract
Aptamers are short oligonucleotides capable of binding specifically to various targets (i.e., small molecules, proteins, and whole cells) which have been introduced in biosensors such as in the electrochemical aptamer-based (E-AB) sensing platform. E-AB sensors are comprised of a redox-reporter-modified aptamer attached to an electrode that undergoes, upon target addition, a binding-induced change in electron transfer rates. To date, E-AB sensors have faced a limitation in the translatability of aptamers into the sensing platform presumably because sequences obtained from Systematic Evolution of Ligands by Exponential Enrichment (SELEX) are typically long (> 80 nucleotides) and that obtaining structural information remains time and resource consuming. In response, we explore the utility of aptamer base truncations and in silico docking to improve their translatability into E-AB sensors. Here, we first apply this to the glucose aptamer, which we characterize in solution using NMR methods to guide design and translate truncated variants in E-AB biosensors. We further investigated applicability of the truncation and computational approaches to five other aptamer systems (vancomycin, cocaine, methotrexate, theophylline, and ochratoxin A) from which we derived functional E-AB sensors. We foresee that our strategy will increase the success rate of translating aptamers into sensing platforms to afford low-cost measurements of molecules directly in undiluted complex matrices.
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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 | 0.000 |
| 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 | 0.001 | 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".