string-database-ppi

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

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

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

# 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

# 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

# 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

# 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

# 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

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

# 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)

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

STRING_API = "https://string-db.org/api"
def string_query(endpoint, params, fmt="tsv"):