tooluniverse-protein-interactions

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsAudit pendingSnapshot 04a0859e3d88

Summary

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SKILL.md

Protein Interaction Network Analysis

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.

Features

✅ 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)

Databases Used

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

Quick Start

Basic Usage

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!

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']})")

# 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']}")
else:
    print("⚠️  Proteins may be unrelated")

result = analyze_protein_network(
    tu=tu,
    proteins=["MAPK1", "MAPK3", "RAF1", "MAP2K1"],  # MAPK pathway
    confidence_score=0.7
)

# Show top enriched processes
print("\nTop Enriched Pathways:")
for term in result.enriched_terms[:10]:
    print(f"  {term['term']}: p={term['p_value']:.2e}, FDR={term['fdr']:.2e}")

# Apoptosis regulators
proteins = ["TP53", "BCL2", "BAX", "CASP3", "CASP9"]

result = analyze_protein_network(
    tu=tu,
    proteins=proteins,
    confidence_score=0.7
)

# Export network for Cytoscape
import pandas as pd
df = pd.DataFrame(result.network_edges)
df.to_csv("apoptosis_network.tsv", sep="\t", index=False)

# Requires BIOGRID_API_KEY in environment
result = analyze_protein_network(
    tu=tu,
    proteins=["TP53", "MDM2"],
    include_biogrid=True  # Enable BioGRID fallback
)

print(f"Primary source: {result.primary_source}")  # "STRING" or "BioGRID"

result = analyze_protein_network(
    tu=tu,
    proteins=["TP53"],
    include_structure=True  # Query SASBDB
)

if result.structural_data:
    print(f"\nFound {len(result.structural_data)} SAXS/SANS entries:")
    for entry in result.structural_data:
        print(f"  {entry.get('sasbdb_id')}: {entry.get('title')}")

Parameter	Type	Default	Description
`tu`	ToolUniverse	Required	ToolUniverse instance
`proteins`	list[str]	Required	Protein identifiers (gene symbols, UniProt IDs)
`species`	int	9606	NCBI taxonomy ID (9606=human, 10090=mouse)
`confidence_score`	float	0.7	Min interaction confidence (0-1). 0.4=low, 0.7=high, 0.9=very high
`include_biogrid`	bool	False	Use BioGRID if STRING fails (requires API key)
`include_structure`	bool	False	Include SASBDB structural data (slower)
`suppress_warnings`	bool	True	Suppress ToolUniverse loading warnings

Score	Level	Description	Use Case
0.15	Very low	All evidence	Exploratory, hypothesis generation
0.4	Low	Medium evidence	Default STRING threshold
0.7	High	Strong evidence	Recommended - reliable interactions
0.9	Very high	Strongest evidence	Core interactions only

@dataclass
class ProteinNetworkResult:
    # Phase 1: Identifier mapping
    mapped_proteins: List[Dict[str, Any]]
    mapping_success_rate: float

    # Phase 2: Network retrieval
    network_edges: List[Dict[str, Any]]
    total_interactions: int

    # Phase 3: Enrichment analysis
    enriched_terms: List[Dict[str, Any]]
    ppi_enrichment: Dict[str, Any]

    # Phase 4: Structural data (optional)
    structural_data: Optional[List[Dict[str, Any]]]

    # Metadata
    primary_source: str  # "STRING" or "BioGRID"
    warnings: List[str]

{
    "stringId_A": "9606.ENSP00000269305",  # Protein A STRING ID
    "stringId_B": "9606.ENSP00000258149",  # Protein B STRING ID
    "preferredName_A": "TP53",             # Protein A name
    "preferredName_B": "MDM2",             # Protein B name
    "ncbiTaxonId": 9606,                   # Species
    "score": 0.999,                        # Combined confidence (0-1)
    "nscore": 0.0,                         # Neighborhood score
    "fscore": 0.0,                         # Gene fusion score
    "pscore": 0.0,                         # Phylogenetic profile score
    "ascore": 0.947,                       # Coexpression score
    "escore": 0.951,                       # Experimental score
    "dscore": 0.9,                         # Database score
    "tscore": 0.994                        # Text mining score
}

{
    "category": "Process",                  # GO category
    "term": "GO:0006915",                   # GO term ID
    "description": "apoptotic process",     # Term description
    "number_of_genes": 4,                   # Genes in your set
    "number_of_genes_in_background": 1234, # Genes in genome
    "p_value": 1.23e-05,                    # Enrichment p-value
    "fdr": 0.0012,                          # FDR correction
    "inputGenes": "TP53,MDM2,BAX,CASP3"    # Matching genes
}

┌─────────────────────────────────────────────────────────────┐
│ Phase 1: Identifier Mapping                                 │
│ ─────────────────────────────────────────────────────────── │
│ STRING_map_identifiers()                                    │
│   • Validates protein names exist in database              │
│   • Converts to STRING IDs for consistency                 │
│   • Returns mapping success rate                           │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 2: Network Retrieval                                  │
│ ─────────────────────────────────────────────────────────── │
│ PRIMARY: STRING_get_network() (no API key needed)          │
│   • Retrieves all pairwise interactions                    │
│   • Returns confidence scores by evidence type             │
│                                                             │
│ FALLBACK: BioGRID_get_interactions() (if enabled)          │
│   • Used if STRING fails or for validation                 │
│   • Requires BIOGRID_API_KEY                               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 3: Enrichment Analysis                                │
│ ─────────────────────────────────────────────────────────── │
│ STRING_functional_enrichment()                              │
│   • GO terms (Process, Component, Function)                │
│   • KEGG pathways                                           │
│   • Reactome pathways                                       │
│   • FDR-corrected p-values                                  │
│                                                             │
│ STRING_ppi_enrichment()                                     │
│   • Tests if proteins interact more than random            │
│   • Returns p-value for functional coherence               │
└─────────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────────┐
│ Phase 4: Structural Data (Optional)                         │
│ ─────────────────────────────────────────────────────────── │
│ SASBDB_search_entries()                                     │
│   • SAXS/SANS solution structures                           │
│   • Protein flexibility and conformations                   │
│   • Complements crystal/cryo-EM data                       │
└─────────────────────────────────────────────────────────────┘

# Install ToolUniverse (if not already installed)
pip install tooluniverse

# Or with extras
pip install tooluniverse[all]

tooluniverse-protein-interactions/
├── SKILL.md                    # This file
├── python_implementation.py    # Main implementation
├── QUICK_START.md             # Quick reference
├── DOMAIN_ANALYSIS.md         # Design rationale
├── PHASE2_COMPLETE.md         # Tool testing results
├── PHASE4_IMPLEMENTATION_COMPLETE.md
└── KNOWN_ISSUES.md            # ToolUniverse limitations

python your_script.py 2>&1 | grep -v "Error loading tools"

Operation	Time	Notes
Identifier mapping	1-2 sec	For 5 proteins
Network retrieval	2-3 sec	Depends on network size
Enrichment analysis	3-5 sec	For 374 terms
Full 4-phase analysis	6-10 sec	Excluding ToolUniverse overhead

tooluniverse-protein-interactions

Summary

Protein Interaction Network Analysis

Features

Databases Used

Quick Start

Basic Usage

Expected Output

Use Cases

1. Single Protein Analysis

2. Protein Complex Validation

3. Pathway Discovery

4. Multi-Protein Network Analysis

5. With BioGRID Validation

6. Including Structural Data

Parameters

analyze_protein_network() Parameters

Species IDs (Common)

Confidence Score Guidelines

Results Structure

ProteinNetworkResult Object

Network Edge Format (STRING)

Enrichment Term Format

Workflow Details

4-Phase Analysis Pipeline

Installation & Setup

Prerequisites

Optional: BioGRID API Key

Skill Files

Known Limitations

1. ToolUniverse Verbose Output

2. BioGRID Requires API Key

3. SASBDB May Have API Issues

Performance

Typical Execution Times

Optimization Tips

Troubleshooting

"Error: 'protein_ids' is a required property"

No interactions found

BioGRID not working

Slow performance

Examples

References

Support

License

Related skills

`analyze_protein_network()` Parameters

`ProteinNetworkResult` Object