A genotype-to-phenotype map of in vitro selected RNA-cleaving DNAzymes: implications for accessing the target phenotype
Bibliographic record
Abstract
Herein, we describe a case study into the population dynamics of in vitro selection, using RNA-cleaving DNAzymes as a model system. We sought to understand how the composition of the population can change over time in response to different levels of selection pressure, and how well these changes are correlated with selection of the target phenotype. The model population is composed of 857 DNAzyme clones representing 215 discrete sequence classes, which had previously been identified from two parallel selection experiments, conducted under an increasingly stringent, or permissive and constant selection time pressure. In this report, we determined the principal phenotypic properties (i.e. k(obs), maximum cleavage yield and PCR efficiency) from a sample of 58 clones representing 46 different major and minor sequence classes from various rounds of each selection experiment. Interestingly, a positive correlation between the catalytic rate constant and the corresponding frequency and temporal position of a given DNAzyme was not consistently observed; however, the strength of the correlation was qualitatively higher under conditions of more stringent selection time pressure. These results suggest that the selective sampling paradigm on which in vitro selection is based, may underestimate the true functional capacity of any given random-sequence library.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".