tooluniverse-gpcr-structural-pharmacology

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)

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

Phase 1: GPCR Receptor Identification

Phase 2: Ligand Landscape

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

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

Phase 5: Antibody Structure Retrieval

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

Pattern 2: Antibody-Antigen Interface Analysis

Pattern 3: GPCR Family Survey

Pattern 4: Structure Interface Characterization

Tool Combinations with Other Skills

This skill complements other ToolUniverse skills:

Fallback Chains

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