Determinants of physical and functional coupling between Thromboxane A2 receptor and Gαq
Bibliographic record
Abstract
G protein coupled receptors (GPCRs) interact with heterotrimeric G proteins and initiate a wide variety of signaling pathways. The molecular nature of GPCR‐G protein interaction in the clinically important thromboxane A2 receptor (TP) is poorly understood. TP activates its cognate G‐protein (Gαq) in response to binding of thromboxane and mediates vasoconstriction and thrombosis. Although quite a large number of biophysical and biochemical studies have shown the possible regions and/or residues important in GPCR‐G protein interactions here we attempt to elucidate the determinants of physical and functional coupling between TP and Gαq. We have constructed TP with minimal number of cysteines by replacing 4 of the endogenous and non‐essential cysteines (4‐Cys). From molecular modeling analysis, the TP amino acids predicted to interact with Gαq were replaced with cysteines one at a time, using the 4‐Cys TP as the base receptor. Disulphide cross linking between TP cysteine mutants and C‐terminal Gαq cysteine mutants are being pursued using Copper phenanthroline in presence of TP specific agonist U46619 under reducing and non ‐ reducing conditions. The crosslinked regions will be analyzed by western blots using TP and Gαq specific antibodies. The results will enable us to understand the dynamics of GPCR‐G protein interactions. Supported by MHRC, MICH/faculty of dentistry Fellowship to RC, HSF and MHRC to PC
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".