tooluniverse-gpcr-structural-pharmacology

What this skill does

# GPCR and Structural Pharmacology Research

**GPCR pharmacology**: agonist vs antagonist vs inverse agonist vs biased agonist — each has different clinical implications. Biased agonism (preferential G-protein vs β-arrestin signaling) can separate efficacy from side effects; for example, G-protein-biased opioid agonists aim to retain analgesia while reducing β-arrestin-mediated respiratory depression. Always classify retrieved ligands by their pharmacological type, not just their chemical structure. Receptor state (active vs inactive crystal structure) determines which ligands and mutations are interpretable — an inactive-state structure is appropriate for antagonist binding analysis, active-state for agonist-bound complexes. Generic GPCR numbering (Ballesteros-Weinstein) enables cross-receptor mutation comparison; always report positions in this system alongside sequence positions.

**LOOK UP DON'T GUESS**: never assume GPCRdb entry names (e.g., `adrb2_human`) or PDB IDs — always use `GPCRdb_list_proteins` to find the correct entry name and `GPCRdb_get_structures` to confirm available structures.

Research skill integrating GPCRdb (GPCR receptor biology), SAbDab (antibody structures), and PDBePISA (protein interface analysis) to support structural pharmacology, antibody engineering, and GPCR-targeted drug discovery.

**KEY PRINCIPLES**:
1. **Receptor-first** — Identify GPCR entry name before any GPCRdb queries
2. **Ligand classification** — Distinguish agonists, antagonists, partial agonists, biased agonists
3. **Structure-guided** — Pair GPCRdb mutation data with PDB structures via PDBePISA
4. **Antibody context** — Use SAbDab for therapeutic antibody structure retrieval and CDR analysis
5. **English-first queries** — Use standard receptor names (e.g., "beta-2 adrenergic receptor") in searches; convert to GPCRdb entry names for API calls

---

## When to Use

Apply when user asks:
- "What ligands are known for [GPCR receptor]?"
- "What crystal structures exist for [receptor]?"
- "Find antibody structures targeting [antigen]"
- "Analyze the protein-protein interface in PDB [ID]"
- "What mutations affect [GPCR] function or pharmacology?"
- "Which GPCRs are in the [family] family?"
- "What are the CDR loops in antibody PDB [ID]?"
- "What is the biological assembly for [PDB ID]?"

---

## Tool Parameter Reference (CRITICAL)

| Tool | Key Parameters | Notes |
|------|---------------|-------|
| `GPCRdb_get_protein` | `protein` | GPCRdb entry name (e.g., `adrb2_human`), NOT gene symbol or UniProt accession |
| `GPCRdb_list_proteins` | `family` (optional), `protein_class` (optional) | Lists all GPCRs; filter by family slug (e.g., `"adrenoceptors"`) OR by human-readable class name via `protein_class` (e.g., `"chemokine receptors"`, `"opioid receptors"`) |
| `GPCRdb_get_structures` | `protein` (optional), `state` (optional) | `state`: `"active"`, `"inactive"`, `"intermediate"` |
| `GPCRdb_get_ligands` | `protein` | Returns agonists, antagonists, biased ligands with affinities |
| `GPCRdb_get_mutations` | `protein` | Returns mutation effects on receptor function and ligand binding |
| `SAbDab_search_structures` | `query` | Antigen name, species, or keywords; returns browse URL + metadata |
| `SAbDab_get_structure` | `pdb_id` | 4-character PDB code (e.g., `"6W41"`); returns CDR annotations |
| `SAbDab_get_summary` | (no required params) | Database statistics and summary |
| `PDBePISA_get_interfaces` | `pdb_id` | 4-character PDB code; returns all interface pairs with buried area |
| `PDBePISA_get_assemblies` | `pdb_id` | Predicted biological assemblies from crystal packing |
| `PDBePISA_get_monomer_analysis` | `pdb_id` | Per-chain solvent-accessible surface area (SASA) breakdown |

### GPCRdb Entry Name Format

GPCRdb uses its own entry name format: `{receptor_slug}_{species}`. Common examples:
- Beta-2 adrenergic receptor: `adrb2_human`
- Beta-1 adrenergic receptor: `adrb1_human`
- Mu-opioid receptor: `oprm1_human`
- Dopamine D2 receptor: `drd2_human`
- Glucagon-like peptide-1 receptor: `glp1r_human`
- CXCR4 chemokine receptor: `cxcr4_human`

If entry name is unknown, use `GPCRdb_list_proteins()` to browse and find the correct slug. You can also filter by receptor class using the `protein_class` parameter with a human-readable name — e.g., `GPCRdb_list_proteins(protein_class="chemokine receptors")` — instead of the numeric family slug. Both `family` and `protein_class` are accepted and serve overlapping purposes; prefer `protein_class` when the user provides a receptor class name.

---

## Workflow Overview

```
Phase 1: Receptor Identification (for GPCR queries)
  -> GPCRdb_list_proteins: find receptor family and entry name
  -> GPCRdb_get_protein: receptor details, family, species

Phase 2: Ligand Landscape
  -> GPCRdb_get_ligands: all known ligands by pharmacology class
  -> Cross-reference with ChEMBL/PubChem for chemical properties

Phase 3: Structural Data
  -> GPCRdb_get_structures: available PDB/EMDB structures with resolution
  -> PDBePISA_get_interfaces: interface analysis on best structure
  -> PDBePISA_get_assemblies: biological assembly determination

Phase 4: Mutation & Pharmacology Data
  -> GPCRdb_get_mutations: pharmacological mutation map
  -> Compare to ligand binding sites from structure

Phase 5: Antibody Structures (for antibody queries)
  -> SAbDab_search_structures: find structures by antigen
  -> SAbDab_get_structure: CDR annotations, chain details
  -> PDBePISA_get_interfaces: antibody-antigen interface analysis
```

---

## Phase 1: GPCR Receptor Identification

```python
# List all GPCRs in a family to find entry name (by slug)
family_list = GPCRdb_list_proteins(family="adrenoceptors")

# Filter by human-readable class name (new -- preferred when user says e.g. "chemokine receptors")
chemokine_list = GPCRdb_list_proteins(protein_class="chemokine receptors")

# Browse all GPCRs (no family filter)
all_gpcrs = GPCRdb_list_proteins()

# Get detailed protein info once you have the entry name
receptor = GPCRdb_get_protein(protein="adrb2_human")
# Returns: family classification, endogenous ligands, tissue expression,
#          GPCRdb-specific annotations, sequence features
```

## Phase 2: Ligand Landscape

```python
# Get all known ligands for a GPCR
ligands = GPCRdb_get_ligands(protein="adrb2_human")
# Returns: ligand names, types (agonist/antagonist/partial/biased/allosteric),
#          binding affinities (Ki, IC50, EC50), references

# Ligand type classification:
# - Agonist: activates receptor
# - Antagonist/Inverse agonist: blocks or suppresses receptor
# - Partial agonist: submaximal activation
# - Biased agonist: selective signaling (Gs vs. beta-arrestin bias)
# - Positive/Negative allosteric modulator (PAM/NAM)
```

After retrieving ligands from GPCRdb, optionally cross-reference with:
- `PubChem_get_CID_by_compound_name(compound_name=ligand_name)` — get CID, SMILES
- `ChEMBL_search_molecules(query=ligand_name)` — get ChEMBL ID, bioactivity data

## Phase 3: Structural Data

```python
# Get available crystal/cryo-EM structures
structures = GPCRdb_get_structures(protein="adrb2_human", state="inactive")
# state options: "active", "inactive", "intermediate" (omit for all)
# Returns: PDB IDs, resolution, ligand in structure, publication info

# Analyze a specific structure's interfaces
interfaces = PDBePISA_get_interfaces(pdb_id="2rh1")  # adrb2 inactive structure
# Returns: interface pairs, buried solvent-accessible area (BSA),
#          interface residues, hydrogen bonds, salt bridges

# Determine biological assembly
assemblies = PDBePISA_get_assemblies(pdb_id="2rh1")
# Returns: predicted oligomeric state, assembly stability score,
#          subunit composition

# Per-chain SASA breakdown
monomers = PDBePISA_get_monomer_analysis(pdb_id="2rh1")
# Returns: accessible/buried surface area per chain
```

**Interface Analysis Interpretation**:
- BSA > 1500 Ų: Strong interface (likely biologically relevant)
- BSA 800-1500 Ų: Moder