Structural insights into the agonist selectivity and structure-based engineering of the adenosine A3 receptor
Bibliographic record
Abstract
Abstract Adenosine receptors, expressed across various tissues, play pivotal roles in physiological processes and are implicated in diverse diseases, including neurological disorders and inflammation, highlighting the therapeutic potential of receptor-selective agents. The Adenosine A3 receptor (A 3 R), the last identified adenosine receptor, is also activated by breakdown products of post-transcriptionally modified tRNA and exhibits dual roles in neuron, heart, and immune cells, and is often overexpressed in tumors, making it a target for anticancer therapy. Despite extensive studies on the other adenosine receptors, the structure and activation mechanism of A 3 R, especially by selective agonists like N 6 -methyladenosine (m 6 A) and namodenoson, remained elusive. Here, we identified N 6 -isopentenyl adenosine (i 6 A), a novel A 3 R-selective ligand, via comprehensive modified adenosine library screening. Cryo-EM analyses of A 3 R-G i signaling complexes with two nonselective and three selective agonists revealed the structural basis for A 3 R activation. We further conducted structure-guided engineering of m 6 A-insensitive A 3 R, which would greatly facilitate future discoveries of the physiological functions of the selective activation of A 3 R by modified adenosines. Our results clarify the selective activation of adenosine receptors, providing the basis for future drug discovery.
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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".