Structural Basis of Activation of Bitter Taste Receptor T2R1 and Comparison with Class A G-protein-coupled Receptors (GPCRs)
Bibliographic record
Abstract
The human bitter taste receptors (T2Rs) are non-Class A members of the G-protein-coupled receptor (GPCR) superfamily, with very limited structural information. Amino acid sequence analysis reveals that most of the important motifs present in the transmembrane helices (TM1-TM7) of the well studied Class A GPCRs are absent in T2Rs, raising fundamental questions regarding the mechanisms of activation and how T2Rs recognize bitter ligands with diverse chemical structures. In this study, the bitter receptor T2R1 was used to systematically investigate the role of 15 transmembrane amino acids in T2Rs, including 13 highly conserved residues, by amino acid replacements guided by molecular modeling. Functional analysis of the mutants by calcium imaging analysis revealed that replacement of Asn-66(2.65) and the highly conserved Asn-24(1.50) resulted in greater than 90% loss of agonist-induced signaling. Our results show that Asn-24(1.50) plays a crucial role in receptor activation by mediating an hydrogen bond network connecting TM1-TM2-TM7, whereas Asn-66(2.65) is essential for binding to the agonist dextromethorphan. The interhelical hydrogen bond between Asn-24(1.50) and Arg-55(2.54) restrains T2R receptor activity because loss of this bond in I27A and R55A mutants results in hyperactive receptor. The conserved amino acids Leu-197(5.50), Ser-200(5.53), and Leu-201(5.54) form a putative LXXSL motif which performs predominantly a structural role by stabilizing the helical conformation of TM5 at the cytoplasmic end. This study provides for the first time mechanistic insights into the roles of the conserved transmembrane residues in T2Rs and allows comparison of the activation mechanisms of T2Rs with the Class A GPCRs.
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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".