An Introduction to Molecular Modeling of G-Protein Coupled Receptors
Bibliographic record
Abstract
G-protein coupled receptors (GPCRs) are the largest single family of signaling molecules in mammals and represent approximately 2-3% of all genes in the human genome. Estimates of the total number of GPCR genes in the human genome range from about 750 to 1000. GPCRs mediate signaling by a wide variety of ligands including amino acids, ions, biogenic amines, peptides, glycoproteins, light, pheromones, and odorants. There are presently only a handful of GPCRs whose structures have been elucidated. Of these, the mGluR1 subtype of metabotropic glutamate receptor (mGluR) and rhodopsin are the most widely used in modeling GPCRs. In the case of mGluR1, the three-dimensional structure of the extracellular ligand binding domain of the molecule has been solved, while the crystallographic data for rhodopsin encompasses the whole protein in the ground state with bound 11-cis-retinal. In this review, we discuss the use of homology modeling to investigate the structures and functions of GPCRs. We illustrate the use of homology modeling with a particular emphasis on ligand and drug binding sites in the Family C subfamily of GPCRs. Keywords: Homology modeling, ligand-receptor interaction, ligand docking, mGluR, Family C GPCRs, cysteine-rich domain, allosteric modulato
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".