STRING Database — Protein-Protein Interactions

Overview

Query the STRING protein-protein interaction database (59M proteins, 20B+ interactions, 5000+ species) via REST API. Covers network retrieval, functional enrichment (GO, KEGG, Pfam), interaction partner discovery, PPI enrichment testing, network visualization, and homology analysis.

When to Use

• Retrieving protein-protein interaction networks for one or multiple proteins
• Performing functional enrichment analysis (GO, KEGG, Pfam, InterPro) on protein lists
• Discovering interaction partners and expanding protein networks from seed proteins
• Testing whether a set of proteins forms a significantly enriched functional module
• Generating network visualizations with evidence-based coloring
• Analyzing homology and protein family relationships across species
• Identifying hub proteins and network connectivity patterns
• For chemical compound interactions use chembl-database-bioactivity instead; for pathway-centric queries use kegg-database

Prerequisites

bash
uv pip install requests pandas

Rate limiting: No strict rate limit, but wait ~1 second between API calls. For proteome-scale analyses, use bulk downloads from https://string-db.org/cgi/download instead of the API.

Quick Start

python
import requests
import time

STRING_API = "https://string-db.org/api"

def string_query(endpoint, params, fmt="tsv"):
    """Reusable helper for all STRING API calls."""
    url = f"{STRING_API}/{fmt}/{endpoint}"
    params.setdefault("caller_identity", "python_script")
    response = requests.get(url, params=params)
    response.raise_for_status()
    return response.text

# Map gene names to STRING IDs (always do this first)
result = string_query("get_string_ids", {
    "identifiers": "TP53\nBRCA1\nEGFR",
    "species": 9606
})
print(result)

# Get interaction network
time.sleep(1)
network = string_query("network", {
    "identifiers": "TP53%0dBRCA1%0dMDM2",
    "species": 9606,
    "required_score": 400
})
print(network[:500])

Key Concepts

Common Species NCBI Taxon IDs

| Organism | Common Name | Taxon ID | |----------|-------------|----------| | Homo sapiens | Human | 9606 | | Mus musculus | Mouse | 10090 | | Rattus norvegicus | Rat | 10116 | | Drosophila melanogaster | Fruit fly | 7227 | | Caenorhabditis elegans | C. elegans | 6239 | | Saccharomyces cerevisiae | Yeast | 4932 | | Arabidopsis thaliana | Thale cress | 3702 | | Escherichia coli K-12 | E. coli | 511145 | | Danio rerio | Zebrafish | 7955 | | Gallus gallus | Chicken | 9031 |

Full species list: https://string-db.org/cgi/input?input_page_active_form=organisms

STRING Identifier Format

STRING uses Ensembl protein IDs with taxon prefix: {taxonId}.{ensemblProteinId} (e.g., 9606.ENSP00000269305 for human TP53). Always map gene names to STRING IDs first via get_string_ids for faster subsequent queries.

Interaction Confidence Scores

Combined scores (0-1000) integrating 7 evidence channels:

| Channel | Code | Source | |---------|------|--------| | Neighborhood | nscore | Conserved genomic neighborhood | | Fusion | fscore | Gene fusion events | | Phylogenetic profile | pscore | Co-occurrence across species | | Coexpression | ascore | Correlated RNA expression | | Experimental | escore | Biochemical/genetic experiments | | Database | dscore | Curated pathway/complex databases | | Text-mining | tscore | Literature co-occurrence and NLP |

Recommended thresholds:

• 150: Low confidence (exploratory, hypothesis generation)
• 400: Medium confidence (standard analysis, default)
• 700: High confidence (conservative, fewer false positives)
• 900: Highest confidence (very stringent, experimental evidence preferred)

Network Types

• Functional (default): All evidence types — proteins functionally associated even without direct binding. Use for pathway analysis, enrichment, systems biology
• Physical: Direct binding evidence only — experimental data and curated physical interactions. Use for structural studies, complex analysis

Output Formats

Replace /tsv/ in the URL with the desired format:

• TSV: Tab-separated (default, best for data processing)
• JSON: Structured data (/json/)
• PNG/SVG: Network images (/image/)
• PSI-MI/PSI-MITAB: Proteomics standard formats

Core API

1. Identifier Mapping

python
# Map gene names to STRING IDs
result = string_query("get_string_ids", {
    "identifiers": "TP53\nBRCA1\nEGFR",
    "species": 9606,
    "limit": 1,        # matches per identifier
    "echo_query": 1    # include query term in output
})

# Parse the mapping
import pandas as pd
import io
df = pd.read_csv(io.StringIO(result), sep='\t')
id_map = dict(zip(df['queryItem'], df['stringId']))
print(id_map)
# {'TP53': '9606.ENSP00000269305', 'BRCA1': '9606.ENSP00000...', ...}

2. Network Retrieval

python
# Get PPI network with confidence scores
network = string_query("network", {
    "identifiers": "TP53%0dBRCA1%0dMDM2%0dATM%0dCHEK2",
    "species": 9606,
    "required_score": 400,
    "network_type": "functional"  # or "physical"
})

# Parse network edges
time.sleep(1)
df = pd.read_csv(io.StringIO(network), sep='\t')
print(f"Found {len(df)} interactions")
print(df[['preferredName_A', 'preferredName_B', 'score']].head())

# Expand network with additional interactors
expanded = string_query("network", {
    "identifiers": "TP53",
    "species": 9606,
    "add_nodes": 10,  # add 10 most connected proteins
    "required_score": 700
})

3. Network Visualization

python
# Get PNG network image
url = f"{STRING_API}/image/network"
params = {
    "identifiers": "TP53%0dMDM2%0dATM%0dCHEK2%0dBRCA1",
    "species": 9606,
    "required_score": 700,
    "network_flavor": "evidence",  # "evidence", "confidence", or "actions"
    "caller_identity": "python_script"
}
response = requests.get(url, params=params)
with open("network.png", "wb") as f:
    f.write(response.content)

4. Interaction Partners

python
# Discover top interaction partners
partners = string_query("interaction_partners", {
    "identifiers": "TP53",
    "species": 9606,
    "limit": 20,
    "required_score": 700
})

df = pd.read_csv(io.StringIO(partners), sep='\t')
print(f"Top 20 TP53 interactors:")
print(df[['preferredName_B', 'score']].head(10))

5. Functional Enrichment

python
# GO, KEGG, Pfam, InterPro, SMART, UniProt Keywords enrichment
# Statistical method: Fisher's exact test with Benjamini-Hochberg FDR correction
enrichment = string_query("enrichment", {
    "identifiers": "TP53%0dMDM2%0dATM%0dCHEK2%0dBRCA1%0dATR%0dTP73",
    "species": 9606
})

df = pd.read_csv(io.StringIO(enrichment), sep='\t')
significant = df[df['fdr'] < 0.05]
print(f"Significant terms: {len(significant)}")

# Group by annotation category
for cat, group in significant.groupby('category'):
    print(f"\n{cat}: {len(group)} terms")
    for _, row in group.head(3).iterrows():
        print(f"  {row['description']} (FDR={row['fdr']:.2e})")

6. PPI Enrichment Testing

python
import json

# Test if proteins form a significant functional module
result = string_query("ppi_enrichment", {
    "identifiers": "TP53%0dMDM2%0dATM%0dCHEK2%0dBRCA1",
    "species": 9606,
    "required_score": 400
}, fmt="json")

data = json.loads(result)
print(f"Observed edges: {data['number_of_edges']}")
print(f"Expected edges: {data['expected_number_of_edges']}")
print(f"P-value: {data['p_value']}")
# p < 0.05 → proteins form a significantly enriched network

7. Homology Scores

python
# Get homology/similarity between proteins
homology = string_query("homology", {
    "identifiers": "TP53%0dTP63%0dTP73",
    "species": 9606
})
print(homology)

Common Workflows

Workflow 1: Protein List Analysis (Standard)

python
import requests, pandas as pd, io, json, time

STRING_API = "https://string-db.org/api"
def string_query(endpoint, params, fmt="tsv"):
    url = f"{STRING_API}/{fmt}/{endpoint}"
    params.setdefault("caller_identity", "python_script")
    response = requests.get(url, params=params)
    response.raise_for_status()
    time.sleep(1)
    return response.text

genes = "TP53%0dBRCA1%0dATM%0dCHEK2%0dMDM2%0dATR%0dBRCA2"

# Step 1: Map identifiers
mapping = string_query("get_string_ids", {"identifiers": genes.replace("%0d", "\n"), "species": 9606})

# Step 2: Get interaction network
network = string_query("network", {"identifiers": genes, "species": 9606, "required_score": 400})
net_df = pd.read_csv(io.StringIO(network), sep='\t')
print(f"Network: {len(net_df)} interactions")

# Step 3: Test PPI enrichment
ppi = json.loads(string_query("ppi_enrichment", {"identifiers": genes, "species": 9606}, fmt="json"))
print(f"PPI enrichment p-value: {ppi['p_value']}")

# Step 4: Functional enrichment
enrich = string_query("enrichment", {"identifiers": genes, "species": 9606})
enrich_df = pd.read_csv(io.StringIO(enrich), sep='\t')
sig = enrich_df[enrich_df['fdr'] < 0.05]
print(f"Significant GO/KEGG terms: {len(sig)}")

# Step 5: Save network image
img_resp = requests.get(f"{STRING_API}/image/network", params={
    "identifiers": genes, "species": 9606, "required_score": 400,
    "network_flavor": "evidence", "caller_identity": "python_script"
})
with open("protein_network.png", "wb") as f:
    f.write(img_resp.content)

Workflow 2: Network Expansion from Seed Proteins

python
# Start with seed proteins, discover connected functional modules
seed = "TP53"

# Step 1: Get high-confidence interaction partners
partners = string_query("interaction_partners", {
    "identifiers": seed, "species": 9606, "limit": 30, "required_score": 700
})
df = pd.read_csv(io.StringIO(partners), sep='\t')
all_proteins = list(set(df['preferredName_A'].tolist() + df['preferredName_B'].tolist()))
print(f"Expanded network: {len(all_proteins)} proteins")

# Step 2: Enrichment on expanded set
expanded_ids = "%0d".join(all_proteins[:50])
enrichment = string_query("enrichment", {"identifiers": expanded_ids, "species": 9606})
enrich_df = pd.read_csv(io.StringIO(enrichment), sep='\t')
modules = enrich_df[enrich_df['fdr'] < 0.001]
print(f"Highly significant terms: {len(modules)}")

Workflow 3: Cross-Species Comparison

python
# Compare protein interactions across species
for species, name, gene in [(9606, "Human", "TP53"), (10090, "Mouse", "Trp53")]:
    network = string_query("network", {
        "identifiers": gene, "species": species,
        "required_score": 700, "add_nodes": 5
    })
    df = pd.read_csv(io.StringIO(network), sep='\t')
    print(f"{name} ({gene}): {len(df)} interactions at score >= 700")

Common Recipes

Recipe: Parse Enrichment Results to DataFrame

python
import pandas as pd, io

enrichment_tsv = string_query("enrichment", {
    "identifiers": "TP53%0dBRCA1%0dATM", "species": 9606
})
df = pd.read_csv(io.StringIO(enrichment_tsv), sep='\t')
# Columns: category, term, description, number_of_genes, p_value, fdr
kegg = df[df['category'] == 'KEGG'].sort_values('fdr')
print(kegg[['description', 'fdr']].head(5))

Recipe: Batch Protein Queries with Rate Limiting

python
import time

protein_lists = [["TP53", "MDM2"], ["EGFR", "ERBB2"], ["BRCA1", "BRCA2"]]
results = []
for proteins in protein_lists:
    ids = "%0d".join(proteins)
    network = string_query("network", {"identifiers": ids, "species": 9606})
    results.append(network)
    time.sleep(1)  # respect rate limits

Recipe: Version Check for Reproducibility

python
version = string_query("version", {})
print(f"STRING version: {version.strip()}")
# Include in methods section: "STRING v{version}, accessed {date}"

Key Parameters

| Parameter | Endpoint | Default | Description | |-----------|----------|---------|-------------| | identifiers | All | — | Protein IDs, %0d-separated for URL or \n-separated for POST | | species | All | — | NCBI taxon ID (9606=human, 10090=mouse) | | required_score | network, partners, ppi_enrichment | 400 | Confidence threshold 0-1000 | | network_type | network | functional | functional (all evidence) or physical (direct binding) | | add_nodes | network, image | 0 | Additional connected proteins to include (0-10) | | limit | get_string_ids, partners | 1/10 | Max results per query | | network_flavor | image | evidence | evidence, confidence, or actions |

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | No proteins found | Wrong species or identifier typo | Verify species taxon ID; use get_string_ids to check identifier mapping | | Empty network | Too strict confidence threshold | Lower required_score; verify proteins actually interact in STRING | | Timeout on large queries | Too many proteins in single request | Split into batches of 50-100; use bulk downloads for proteome-scale | | "Species required" error | Missing species for >10 protein networks | Always include species parameter | | Unexpected results | Wrong network type or STRING version | Check network_type (functional vs physical); verify version with /version | | 400 Bad Request | Malformed identifiers | Use %0d separator in URL or \n in POST body; URL-encode special characters | | Enrichment returns no terms | Too few input proteins | Enrichment needs 5+ proteins for meaningful results |

Best Practices

• Always map identifiers first — use get_string_ids() before other operations; STRING IDs (e.g., 9606.ENSP00000269305) are faster than gene names
• Rate-limit all requests — add time.sleep(1) between API calls
• Choose appropriate thresholds — 400 for exploratory analysis, 700 for publications, 900 for high-confidence only
• Specify species explicitly — required for networks >10 proteins, recommended always
• Use functional networks for pathway analysis and enrichment; physical networks for structural biology and direct binding
• Include version in methods — check string_version() for reproducibility

Related Skills

• networkx-graph-analysis — Graph analysis and visualization of STRING interaction networks
• kegg-database — Pathway-centric queries complementary to STRING enrichment
• bioservices-multi-database — Alternative access to STRING via the PSICQUIC interface

References

• STRING website: https://string-db.org
• API documentation: https://string-db.org/help/api/
• Download page: https://string-db.org/cgi/download
• Publications: https://string-db.org/cgi/about

Bundled Resources

Main SKILL.md + 1 reference file. Original total: 990 lines (SKILL.md 534 + string_reference.md 456). Scripts: 370 lines (string_api.py).

references/api_advanced.md: Advanced API features (values/ranks enrichment, bulk upload, R/Cytoscape integration), output format details, HTTP error codes, data license — content from original string_reference.md that exceeds Core API scope.

Original file disposition:

• SKILL.md (534 lines) → Core API modules 1-7, Workflows 1-3, Quick Start helper function, Key Concepts (species table, score thresholds, network types). "Common Use Cases" per-operation subsections consolidated into Core API module descriptions (rule 7b): each operation's "When to use" and "Use cases" → Core API intro text. "Detailed Reference" stub section → removed, content consolidated inline
• references/string_reference.md (456 lines) → Partially consolidated inline: API endpoints → Core API modules with code blocks; species table → Key Concepts; confidence scores → Key Concepts; identifier format → Key Concepts. Advanced features (values/ranks enrichment, bulk upload), integration examples (R STRINGdb, Cytoscape), output format details, HTTP error codes, data license → migrated to references/api_advanced.md
• scripts/string_api.py (370 lines) → Helper function pattern absorbed into Quick Start (string_query reusable function). Per-function disposition: string_map_ids → Core API Module 1; string_network → Module 2; string_network_image → Module 3; string_interaction_partners → Module 4; string_enrichment → Module 5; string_ppi_enrichment → Module 6; string_homology → Module 7; string_version → Recipe. All were thin wrappers around urllib; replaced with requests-based string_query helper

Retention: ~460 lines (SKILL.md) + ~180 lines (reference) = ~640 / 990 original = ~65%.