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Protein Interaction Network Analysis - Domain Analysis

Date: 2026-02-12 Phase: 1 - Domain Analysis

Skill Overview

Purpose: Analyze protein interaction networks to understand biological systems, identify key regulatory proteins, and discover functional relationships.

Target Users:

  • Systems biologists studying protein networks
  • Drug discovery researchers identifying drug targets
  • Computational biologists analyzing omics data
  • Researchers investigating disease mechanisms

Concrete Use Cases

Use Case 1: Single Protein Analysis

Query: "What proteins interact with TP53?" Expected Workflow:

  1. Map "TP53" to protein identifier (STRING ID)
  2. Retrieve direct interaction partners (BioGRID + STRING)
  3. Get functional enrichment (pathways, GO terms)
  4. Generate network report with interaction confidence scores

Expected Output:

  • List of interacting proteins with confidence scores
  • Pathways enriched in the network
  • GO terms associated with interactions
  • Network visualization data

Use Case 2: Multi-Protein Network

Query: "Analyze the interaction network for TP53, MDM2, ATM" Expected Workflow:

  1. Map all protein names to identifiers
  2. Retrieve pairwise interactions between proteins
  3. Build complete interaction network
  4. Identify hubs and key regulators
  5. Perform functional enrichment analysis

Expected Output:

  • Complete interaction network (nodes + edges)
  • Network statistics (degree, betweenness, clustering)
  • Enriched pathways and biological processes
  • Key regulatory proteins identified

Use Case 3: Disease Pathway Analysis

Query: "Find protein interactions involved in DNA damage response" Expected Workflow:

  1. Search BioGRID by keyword "DNA damage response"
  2. Retrieve interactions from published studies
  3. Map proteins to functional categories
  4. Analyze pathway enrichment
  5. Identify potential drug targets

Expected Output:

  • Proteins involved in DNA damage response
  • Key interaction pairs with evidence
  • Pathway maps and GO terms
  • PubMed citations for interactions

Use Case 4: Chemical-Protein Interactions

Query: "What proteins interact with Cisplatin?" Expected Workflow:

  1. Search BioGRID for chemical interactions
  2. Retrieve protein targets of Cisplatin
  3. Analyze functional consequences
  4. Identify resistance mechanisms

Expected Output:

  • Direct protein targets
  • Interaction types (binding, modification)
  • Cellular pathways affected
  • Literature evidence

Use Case 5: Post-Translational Modifications

Query: "What phosphorylation sites are found on TP53?" Expected Workflow:

  1. Query BioGRID PTM database for TP53
  2. Retrieve phosphorylation sites and kinases
  3. Analyze functional impact
  4. Get literature references

Expected Output:

  • List of PTM sites with positions
  • Kinases that phosphorylate each site
  • Functional consequences
  • Evidence codes and citations

4-Phase Analysis Pipeline

Phase 1: Protein Identification & Mapping

Goal: Convert gene names to standardized protein identifiers

Tools:

  • STRING_map_identifiers - Map gene names to STRING IDs
  • Validation: Check if proteins exist in databases

Input: List of protein names (e.g., ["TP53", "MDM2", "ATM"]) Output: Mapped protein IDs with species confirmation

Phase 2: Interaction Network Retrieval

Goal: Get protein-protein interactions from multiple databases

Tools:

  • BioGRID_get_interactions - Get experimentally validated interactions
  • STRING_get_network - Get functional association network
  • STRING_get_interaction_partners - Get partners for single protein
  • STRING_get_protein_interactions - Alternative interaction retrieval

Input: Protein IDs from Phase 1 Output: Interaction network with confidence scores

Phase 3: Functional Enrichment Analysis

Goal: Identify biological processes and pathways

Tools:

  • STRING_functional_enrichment - Pathway/GO enrichment
  • STRING_ppi_enrichment - PPI enrichment analysis

Input: Network proteins from Phase 2 Output: Enriched pathways, GO terms, statistical significance

Phase 4: Special Analyses (Optional)

Goal: Additional analyses based on user needs

Tools:

  • BioGRID_search_by_pubmed - Find interactions from specific studies
  • BioGRID_get_chemical_interactions - Chemical-protein interactions
  • BioGRID_get_ptms - Post-translational modifications
  • SASBDB_* - Structural data (if needed)

Input: Specific query parameters Output: Specialized analysis results

Database Specifications

BioGRID (Biological General Repository for Interaction Datasets)

  • Coverage: 2.3M+ interactions, 80+ organisms
  • Data Type: Experimentally validated interactions
  • Evidence: Curated from literature with methods
  • API: Requires API key (BIOGRID_API_KEY)

Tools (4):

  1. BioGRID_get_interactions - Get PPI for protein
  2. BioGRID_get_chemical_interactions - Chemical-protein interactions
  3. BioGRID_get_ptms - Post-translational modifications
  4. BioGRID_search_by_pubmed - Find interactions by PubMed ID

STRING (Search Tool for Retrieval of Interacting Genes/Proteins)

  • Coverage: 14M+ proteins, 5,000+ organisms
  • Data Type: Functional associations (experimental + predicted)
  • Confidence: Scores from 0-1000 (combined evidence)
  • API: Public, no key required (rate limits apply)

Tools (6 actual STRING tools):

  1. STRING_map_identifiers - Map names to STRING IDs
  2. STRING_get_network - Get interaction network
  3. STRING_get_interaction_partners - Get partners for protein
  4. STRING_get_protein_interactions - Alternative interaction retrieval
  5. STRING_functional_enrichment - Pathway/GO enrichment
  6. STRING_ppi_enrichment - PPI enrichment statistics

SASBDB (Small Angle Scattering Biological Data Bank)

  • Coverage: 2,000+ entries
  • Data Type: Structural biology (SAXS/SANS)
  • Use Case: Protein structure and complex formation
  • API: Public REST API

Tools (5) - Use for structural analysis:

  1. SASBDB_search_entries - Find structural data
  2. SASBDB_get_entry_data - Get entry metadata
  3. SASBDB_get_models - Get structural models
  4. SASBDB_get_scattering_profile - Get scattering data
  5. SASBDB_download_data - Download raw data

Example Workflows

Workflow 1: Cancer Protein Network (TP53)

Input: protein = "TP53", organism = "Homo sapiens"

Phase 1: Map identifiers
  STRING_map_identifiers("TP53") → "9606.ENSP00000269305"

Phase 2: Get interactions
  BioGRID_get_interactions("TP53") → 450 interactions
  STRING_get_interaction_partners("9606.ENSP00000269305") → 85 high-confidence partners

Phase 3: Functional enrichment
  STRING_functional_enrichment([TP53 + partners]) → DNA repair, apoptosis, cell cycle

Phase 4: PTM analysis
  BioGRID_get_ptms("TP53") → 20 phosphorylation sites

Output: comprehensive_tp53_network.md

Workflow 2: Multi-Protein Complex (DNA Damage Response)

Input: proteins = ["TP53", "ATM", "ATR", "CHEK2"], organism = "Homo sapiens"

Phase 1: Map all identifiers
  STRING_map_identifiers([...]) → 4 STRING IDs

Phase 2: Get complete network
  STRING_get_network([4 proteins], add_neighbors=10) → Network with 50 proteins
  BioGRID_get_interactions for each → 1200 total interactions

Phase 3: Enrichment analysis
  STRING_functional_enrichment([50 proteins]) → DNA damage response pathways

Phase 4: Literature evidence
  BioGRID_search_by_pubmed("DNA damage") → Key papers

Output: dna_damage_network.md

Workflow 3: Drug Target Analysis (Cisplatin)

Input: chemical = "Cisplatin", organism = "Homo sapiens"

Phase 1: Skip (chemical query)

Phase 2: Chemical-protein interactions
  BioGRID_get_chemical_interactions("Cisplatin") → 15 target proteins

Phase 3: Analyze target network
  STRING_get_network([15 targets]) → Extended network
  STRING_functional_enrichment([targets]) → DNA repair, apoptosis

Phase 4: Structural data
  SASBDB_search_entries("cisplatin protein") → Structural complexes

Output: cisplatin_targets.md

Input Parameters

Core Parameters

  • protein_list (list of strings): Gene names or protein IDs

    • Examples: ["TP53"], ["TP53", "MDM2", "ATM"]
    • Optional for chemical/PTM queries

  • organism (string): Scientific name or taxonomy ID

    • Default: "Homo sapiens" (9606)
    • Examples: "Mus musculus", "10090"

  • network_type (string): Type of interactions to retrieve

    • Default: "physical" (BioGRID), "functional" (STRING)
    • Options: "physical", "functional", "both"

Optional Parameters

  • add_neighbors (int): Add N interaction partners to expand network

    • Default: 0 (only input proteins)
    • Range: 0-50

  • confidence_threshold (float): Minimum interaction confidence (STRING)

    • Default: 0.4 (medium confidence)
    • Range: 0.0-1.0

  • pubmed_id (string): Filter by specific PubMed ID (BioGRID)

    • Example: "12345678"

  • chemical_name (string): For chemical-protein interaction queries

    • Example: "Cisplatin"

  • include_ptms (bool): Include PTM analysis

    • Default: False

  • output_file (string): Output markdown file path

    • Default: Auto-generated with timestamp

Expected Report Structure

# Protein Interaction Network Analysis Report

## 1. Protein Identification
- TP53 → 9606.ENSP00000269305 (Homo sapiens)
- MDM2 → 9606.ENSP00000258149 (Homo sapiens)
- Status: 2/2 proteins mapped successfully

## 2. Interaction Network
### BioGRID Interactions (450 total)
- TP53 - MDM2: Physical interaction (Co-IP, Y2H)
- TP53 - ATM: Physical interaction (Western blot)
- [Full list...]

### STRING Network (85 high-confidence)
- TP53 - TP73: 0.912 (experimental + database)
- TP53 - MDM2: 0.999 (experimental evidence)
- [Full list...]

### Network Statistics
- Total proteins: 87
- Total interactions: 535
- Average degree: 12.3
- Network density: 0.142

## 3. Functional Enrichment
### KEGG Pathways
- p53 signaling pathway (FDR: 1.2e-45)
- Cell cycle (FDR: 3.4e-32)
- Apoptosis (FDR: 5.6e-28)

### GO Biological Process
- DNA damage response (FDR: 2.1e-52)
- Regulation of apoptosis (FDR: 4.3e-40)
- Cell cycle checkpoint (FDR: 8.7e-35)

## 4. Post-Translational Modifications (TP53)
- S15: Phosphorylation by ATM, ATR (DNA damage response)
- S20: Phosphorylation by CHEK2 (p53 stabilization)
- [Full list...]

## 5. Key Hub Proteins
1. TP53 (degree: 450) - Tumor suppressor
2. MDM2 (degree: 234) - p53 regulator
3. ATM (degree: 187) - DNA damage sensor

## 6. Literature Evidence
- BioGRID entries: 450 interactions from 320 publications
- Oldest: 1991, Newest: 2025
- Top journals: Nature, Cell, Science, PNAS

Success Criteria

Phase 1 Complete When:

  • ✅ Domain analysis documented with concrete examples
  • ✅ All use cases defined with expected inputs/outputs
  • ✅ Tool inventory complete (17 tools identified)
  • ✅ 4-phase workflow clearly specified
  • ✅ Report structure defined

Next Phase (Phase 2: Tool Testing)

CRITICAL: Test ALL tools BEFORE writing documentation

  • Create test_protein_tools.py
  • Test BioGRID tools (4)
  • Test STRING tools (6)
  • Test SASBDB tools (5)
  • Document actual API responses
  • Identify SOAP vs REST tools
  • Record parameter names and response formats

Notes & Considerations

API Key Requirements

  • BioGRID: Requires BIOGRID_API_KEY (may need to request)
  • STRING: Public API (no key required, rate limits apply)
  • SASBDB: Public API (no key required)

Potential Challenges

  1. BioGRID API Key: Need to check if key is available
  2. Species Mapping: STRING uses taxonomy IDs, need conversion
  3. Network Size: Large networks may be slow to retrieve
  4. Confidence Thresholds: STRING and BioGRID use different scoring

Fallback Strategies

  • Primary: BioGRID (experimental) + STRING (functional)
  • Fallback 1: STRING only (if BioGRID key unavailable)
  • Fallback 2: Basic network without enrichment (if enrichment fails)
  • Default: Report what data is available, note limitations

Comparison to Metabolomics Skill

Similarities

  • 4-phase pipeline structure
  • Multi-database integration
  • Progressive report writing
  • Fallback strategies for missing data

Differences

  • Metabolomics: Small molecules (metabolites)
  • Protein Interactions: Macromolecules (proteins)
  • Metabolomics: 4 databases, 9 tools
  • Protein Interactions: 3 databases, 17 tools
  • Metabolomics: SOAP tools (HMDB)
  • Protein Interactions: Need to verify (likely all REST)

Lessons to Apply

  1. ✅ Test tools BEFORE documentation (caught 3 bugs in Metabolomics)
  2. ✅ Validate actual API response structures (don't assume)
  3. ✅ Create tests that check data presence, not just keywords
  4. ✅ Real-world testing with subagent before release
  5. ✅ Document FIX comments for any parsing corrections

Status: ✅ Phase 1 Complete - Ready for Phase 2 (Tool Testing)