Subunit and small-molecule interaction of ribonucleotide reductases via surface plasmon resonance biosensor analyses
Bibliographic record
Abstract
Ribonucleotide reductase (RNR) synthesizes deoxyribonucleotides for DNA replication and repair and is controlled by sophisticated allosteric regulation involving differential affinity of nucleotides for regulatory sites. We have developed a robust and sensitive method for coupling biotinylated RNRs to surface plasmon resonance streptavidin biosensor chips via a 30.5 A linker. In comprehensive studies on three RNRs effector nucleotides strengthened holoenzyme interactions, whereas substrate had no effect on subunit interactions. The RNRs differed in their response to the negative allosteric effector dATP that binds to an ATP-cone domain. A tight RNR complex was formed in Escherichia coli class Ia RNR with a functional ATP cone. No strengthening of subunit interactions was observed in the class Ib RNR from the human pathogen Bacillus anthracis that lacks the ATP cone. A moderate strengthening was seen in the atypical Aeromonas hydrophila phage 1 class Ia RNR that has a split catalytic subunit and a non-functional ATP cone with remnant dATP-mediated regulatory features. We also successfully immobilized a functional catalytic NrdA subunit of the E.coli enzyme, facilitating study of nucleotide interactions. Our surface plasmon resonance methodology has the potential to provide biological insight into nucleotide-mediated regulation of any RNR, and can be used for high-throughput screening of potential RNR inhibitors.
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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 | 0.000 |
| 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".