Isolation of Fast Purine Nucleotide Synthase Ribozymes
Bibliographic record
Abstract
Here we report the in vitro selection of fast ribozymes capable of promoting the synthesis of a purine nucleotide (6-thioguanosine monophosphate) from tethered 5-phosphoribosyl 1-pyrophosphate (PRPP) and 6-thioguanine ((6S)Gua). The two most proficient purine synthases have apparent efficiencies of 284 and 230 M(-1) min(-1) and are both significantly more efficient than pyrimidine nucleotide synthase ribozymes selected previously by a similar approach. Interestingly, while both ribozymes showed good substrate discrimination, one ribozyme had no detectable affinity for 6-thioguanine while the second had a K(m) of approximately 80 muM, indicating that these ribozymes use considerably different modes of substrate recognition. The purine synthases were isolated after 10 rounds of selection from two high-diversity RNA pools. The first pool contained a long random sequence region. The second pool contained random sequence elements interspersed with the mutagenized helical elements of a previously characterized 4-thiouridine synthase ribozyme. While nearly all of the ribozymes isolated from this biased pool population appeared to have benefited from utilizing one of the progenitor's helical elements, little evidence for more complicated secondary structure preservation was evident. The discovery of purine synthases, in addition to pyrimidine synthases, demonstrates the potential for nucleotide synthesis in an 'RNA World' and provides a context from which to study small molecule RNA catalysis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".