Mechanisms for differentiation between cognate and near-cognate ligands by purine riboswitches
Bibliographic record
Abstract
Riboswitches are elements in the 5'-untranslated region of mRNAs that regulate gene expression by directly interacting with metabolites related to their own gene products. A remarkable feature of this gene regulation mechanism is the high specificity of riboswitches for their cognate ligands. In this study, we used a combination of static and time-resolved NMR-spectroscopic methods to investigate the mechanisms for ligand specificity in purine riboswitches. We investigate the xpt-aptamer domain from a guanine-responsive riboswitch and the mfl-aptamer domain from a 2'-deoxyguanosine-responsive riboswitch. The xpt-aptamer binds the purine nucleobases guanine/hypoxanthine with high affinity, but, unexpectedly, also the nucleoside 2'-deoxyguanosine. On the other hand, the mfl-aptamer is highly specific for its cognate ligand 2'-deoxyguanosine, and does not bind purine ligands. We addressed the question of aptamer`s ligand specificity by real-time NMR spectroscopy. Our studies of ligand binding and subsequently induced aptamer folding revealed that the xpt-aptamer discriminates against non-cognate ligands by enhanced life-times of the cognate complex compared with non-cognate complexes, whereas the mfl-aptamer rejects non-cognate ligands at the level of ligand association, employing a kinetic proofreading mechanism.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".