Convergent olfactory trace amine-associated receptors detect biogenic polyamines with distinct motifs via a conserved binding site
Bibliographic record
Abstract
Biogenic amines activate G-protein-coupled receptors (GPCRs) in the central nervous system in vertebrate animals. Several biogenic amines, when excreted, stimulate trace amine-associated receptors (TAARs), a group of GPCRs in the main olfactory epithelium, and elicit innate behaviors. How TAARs recognize amines with varying numbers of amino groups is largely unknown. We reasoned that a comparison between lamprey and mammalian olfactory TAARs, which are thought to have evolved independently but show convergent responses to polyamines, may reveal structural determinants of amine recognition. Here, we demonstrate that sea lamprey TAAR365 (sTAAR365) responds strongly to biogenic polyamines cadaverine, putrescine, and spermine, and shares a similar response profile as a mammalian TAAR (mTAAR9). Docking and site-directed mutagenesis analyses show that both sTAAR365 and mTAAR9 recognize the two amino groups of cadaverine with the conserved Asp 3.32 and Tyr 6.51 residues. sTAAR365, which has remarkable sensitivity for cadaverine (EC 50 = 4 nM), uses an extra residue, Thr 7.42 , to stabilize ligand binding. These cadaverine recognition sites also interact with amines with four and three amino groups (spermine and spermidine, respectively). Glu 7.36 of sTAAR365 cooperates with Asp 3.32 and Thr 7.42 to recognize spermine, whereas mTAAR9 recognizes spermidine through an additional aromatic residue, Tyr 7.43 . These results suggest a conserved mechanism whereby independently evolved TAAR receptors recognize amines with two, three, or four amino groups using the same recognition sites, at which sTAAR365 and mTAAR9 evolved distinct motifs. These motifs interact directly with the amino groups of the polyamines, a class of potent and ecologically important odorants, mediating olfactory signaling.
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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 | 0.001 |
| 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".