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: Moderate interface
• BSA < 800 Ų: Weak or crystal contact

Phase 4: Mutation Data

python
# Get all mutations characterized for a GPCR
mutations = GPCRdb_get_mutations(protein="adrb2_human")
# Returns: mutation positions (generic GPCR numbering), effects on:
#   - Expression/folding
#   - Ligand binding (affinity changes)
#   - G-protein coupling
#   - Receptor activation

# Generic GPCR numbering (Ballesteros-Weinstein):
# e.g., 3.32 = position 32 in TM helix 3 — conserved across GPCR classes

Phase 5: Antibody Structure Retrieval

python
# Search SAbDab for antibody structures by antigen
results = SAbDab_search_structures(query="EGFR", limit=20)
# Returns: browse URL + metadata table of matching structures

# Get detailed annotations for a specific antibody structure
structure = SAbDab_get_structure(pdb_id="1IQD")
# Returns: VH/VL chain IDs, CDR1-3 (Kabat/IMGT), antigen info,
#          heavy/light chain types, resolution

# Get database overview
summary = SAbDab_get_summary()
# Returns: total structures, species breakdown, antigen coverage stats

CDR Analysis:

• CDR-H3 is most variable and typically dominates antigen contact
• CDR length distribution: SAbDab provides Kabat, Chothia, and IMGT numbering
• After retrieving SAbDab structure, use PDBePISA_get_interfaces(pdb_id=...) to compute antibody-antigen buried surface area

Common Research Patterns

Pattern 1: GPCR Drug Target Profiling

Input: GPCR name (e.g., "GLP-1 receptor")
Flow: GPCRdb_list_proteins -> find "glp1r_human" ->
      GPCRdb_get_protein (receptor details) ->
      GPCRdb_get_ligands (approved + investigational drugs) ->
      GPCRdb_get_structures (available PDB structures) ->
      PDBePISA_get_interfaces on best structure ->
      GPCRdb_get_mutations (pharmacological mutants)
Output: Complete GPCR pharmacology profile with structural context

Pattern 2: Antibody-Antigen Interface Analysis

Input: Target antigen (e.g., "PD-L1") or specific PDB code
Flow: SAbDab_search_structures(query="PD-L1") ->
      SAbDab_get_structure(pdb_id="best hit") (CDR annotations) ->
      PDBePISA_get_interfaces(pdb_id=...) (buried area, key contacts) ->
      PDBePISA_get_assemblies (assembly context)
Output: CDR sequences, epitope contact residues, interface energetics

Pattern 3: GPCR Family Survey

Input: Drug class question (e.g., "beta-adrenergic receptors")
Flow: GPCRdb_list_proteins(family="adrenoceptors") ->
      GPCRdb_get_protein per receptor (adrb1/2/3) ->
      GPCRdb_get_ligands per receptor (selectivity landscape) ->
      GPCRdb_get_structures per receptor (structural coverage)
Output: Family-wide selectivity map, structural availability, ligand classes

Pattern 4: Structure Interface Characterization

Input: PDB code
Flow: PDBePISA_get_assemblies (oligomeric state) ->
      PDBePISA_get_interfaces (all interface pairs ranked by BSA) ->
      PDBePISA_get_monomer_analysis (per-chain surface burial)
Output: Biologically relevant assembly, key interface residues, buried areas

Tool Combinations with Other Skills

This skill complements other ToolUniverse skills:

| Goal | This skill provides | Complement with | |------|--------------------|--------------------| | GPCR drug discovery | Receptor/ligand/structure data | tooluniverse-binder-discovery for virtual screening | | Antibody engineering | SAbDab structure + CDR data | tooluniverse-antibody-engineering for optimization | | Variant impact on GPCR | GPCRdb mutation effects | tooluniverse-variant-functional-annotation for ACMG | | Target validation | GPCR expression, ligand data | tooluniverse-drug-target-validation | | PDB structure analysis | PDBePISA interfaces | tooluniverse-protein-structure-retrieval for RCSB/PDBe |

Fallback Chains

| Primary Tool | Fallback | Use When | |-------------|----------|----------| | GPCRdb_get_protein | UniProt search + PubMed | Entry name unknown or non-GPCR target | | GPCRdb_get_ligands | ChEMBL bioactivity search | Receptor not in GPCRdb | | GPCRdb_get_structures | RCSB PDB text search | Structures not yet in GPCRdb | | SAbDab_search_structures | RCSB PDB antibody search | Antigen not indexed in SAbDab | | PDBePISA_get_interfaces | PDBe graph API | PDBePISA returns no interfaces |

Completeness Checklist

For GPCR profiling:

• ] Entry name resolved via GPCRdb_list_proteins or GPCRdb_get_protein
• ] Receptor family and class documented
• ] Ligand landscape retrieved with pharmacology types
• ] Available structures listed with resolution and state
• ] Best structure analyzed with PDBePISA (interfaces + assembly)
• ] Mutation data retrieved for pharmacological context

For antibody structure:

• ] SAbDab search run with antigen name
• ] Best structure retrieved with SAbDab_get_structure
• ] CDR1-3 sequences extracted for VH and VL chains
• ] Antibody-antigen interface analyzed with PDBePISA
• ] Buried surface area and key contact residues documented

Key References

• GPCRdb: https://gpcrdb.org — standardized GPCR data with generic numbering
• SAbDab: https://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab — structural antibody database
• PDBePISA: https://www.ebi.ac.uk/pdbe/pisa — protein interface analysis