Analyze protein-protein interaction networks using STRING, BioGRID, and SASBDB databases. Maps protein identifiers, retrieves interaction networks with confidence scores, performs functional enrichment analysis (GO/KEGG/Reactome), and optionally includes structural data. No API key required for core functionality (STRING). Use when analyzing protein networks, discovering interaction partners, identifying functional modules, or studying protein complexes.
Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.
✅ Identifier Mapping - Convert protein names to database IDs (STRING, UniProt, Ensembl) ✅ Network Retrieval - Get interaction networks with confidence scores (0-1.0) ✅ Functional Enrichment - GO terms, KEGG pathways, Reactome pathways ✅ PPI Enrichment - Test if proteins form functional modules ✅ Structural Data - Optional SAXS/SANS solution structures (SASBDB) ✅ Fallback Strategy - STRING primary (no API key) → BioGRID secondary (if key available)
| Database | Coverage | API Key | Purpose |
|---|---|---|---|
| STRING | 14M+ proteins, 5,000+ organisms | ❌ Not required | Primary interaction source |
| BioGRID |
| 2.3M+ interactions, 80+ organisms |
| ✅ Required |
| Fallback, curated data |
| SASBDB | 2,000+ SAXS/SANS entries | ❌ Not required | Solution structures |
from tooluniverse import ToolUniverse
from python_implementation import analyze_protein_network
# Initialize ToolUniverse
tu = ToolUniverse()
# Analyze protein network
result = analyze_protein_network(
tu=tu,
proteins=["TP53", "MDM2", "ATM", "CHEK2"],
species=9606, # Human
confidence_score=0.7 # High confidence
)
# Access results
print(f"Mapped: {len(result.mapped_proteins)} proteins")
print(f"Network: {result.total_interactions} interactions")
print(f"Enrichment: {len(result.enriched_terms)} GO terms")
print(f"PPI p-value: {result.ppi_enrichment.get('p_value', 1.0):.2e}")
🔍 Phase 1: Mapping 4 protein identifiers...
✅ Mapped 4/4 proteins (100.0%)
🕸️ Phase 2: Retrieving interaction network...
✅ STRING: Retrieved 6 interactions
🧬 Phase 3: Performing enrichment analysis...
✅ Found 245 enriched GO terms (FDR < 0.05)
✅ PPI enrichment significant (p=3.45e-05)
✅ Analysis complete!
Discover interaction partners for a protein of interest:
result = analyze_protein_network(
tu=tu,
proteins=["TP53"], # Single protein
species=9606,
confidence_score=0.7
)
# Top 5 partners will be in the network
for edge in result.network_edges[:5]:
print(f"{edge['preferredName_A']} ↔ {edge['preferredName_B']} "
f"(score: {edge['score']})")
Test if proteins form a functional complex:
# DNA damage response proteins
proteins = ["TP53", "ATM", "CHEK2", "BRCA1", "BRCA2"]
result = analyze_protein_network(tu=tu, proteins=proteins)
# Check PPI enrichment
if result.ppi_enrichment.get("p_value", 1.0) < 0.05:
print("✅ Proteins form functional module!")
print(f" Expected edges: {result.ppi_enrichment['expected_number_of_edges']:.1f}")
print(f" Observed edges: {result.ppi_enrichment['number_of_edges']}")