E2EDNA 2.0: Python Pipeline for Simulating DNA Aptamerswith Ligands
Bibliographic record
Abstract
DNA aptamers are short sequences of single-stranded DNA with untapped potential in molecular medicine, drug design, and materials design due to their strong and selective and most importantly tunable binding affinity to target molecules (Tucker et al., 2012;Zhou & Rossi, 2017).For instance, DNA aptamers can be used as therapeutics (Corey et al., 2021) for a wide range of diseases such as epilepsy (Zamay et al., 2020) and cancer (Morita et al., 2018).They can also be used to detect a wide variety of molecular ligands, including antibiotics (Mehlhorn et al., 2018), neurotransmitters (Sinha & Das Mukhopadhyay, 2020), metals (Qu et al., 2016), proteins (Kirby et al., 2004), nucleotides (Shen et al., 2007) and metabolites (Dale, 2021;Dauphin-Ducharme et al., 2022) in real time, even in harsh environments (McConnell et al., 2020).We present E2EDNA 2.0: End-2-End DNA 2.0, a Python simulation pipeline that offers a unified and automated solution to computational modeling of DNA aptamers with molecular ligands.It is broadly aimed at researchers developing therapeutics and sensors based on DNA aptamers who require detailed atomistic information on the behavior of aptamers and ligands in realistic media.Similar to its predecessor E2EDNA (Kilgour et al., 2021), E2EDNA 2.0 predicts DNA aptamers' secondary and tertiary structures, and if a ligand is present, the configuration of the solvated aptamer-ligand complex.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".