Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from <i>in Vitro</i> Evolution
Bibliographic record
Abstract
Expanding the chemical diversity of aptamers remains an important thrust in the field in order to increase their functional potential. Previously, our group developed LOOPER, which enables the incorporation of up to 16 unique modifications throughout a ssDNA sequence, and applied it to the in vitro evolution of thrombin binders. As LOOPER-derived highly modified nucleic acids polymers are governed by two interrelated evolutionary variables, namely, functional modifications and sequence, the evolution of this polymer contrasts with that of canonical DNA. Herein we provide in-depth analysis of the evolution, including structure–activity relationships, mapping of evolutionary pressures on the library, and analysis of plausible evolutionary pathways that resulted in the first LOOPER-derived aptamer, TBL1. A detailed picture of how TBL1 interacts with thrombin and how it may mimic known peptide binders of thrombin is also proposed. Structural modeling and folding studies afford insights into how the aptamer displays critical modifications and also how modifications enhance the structural stability of the aptamer. A discussion of benefits and potential limitations of LOOPER during in vitro evolution is provided, which will serve to guide future evolutions of this highly modified class of 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 | 0.001 |
| 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 | 0.000 |
| 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".