string-database-ppi

name	string-database-ppi
description	Query STRING REST API for PPIs (59M proteins, 20B interactions, 5000+ species). Retrieve networks, run GO/KEGG enrichment, find partners, test PPI significance, visualize networks, analyze homology. For chemical interactions use chembl-database-bioactivity; pathways use kegg-database.
license	CC-BY-4.0

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"):
    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(, {: genes, : })
enrich_df = pd.read_csv(io.StringIO(enrich), sep=)
sig = enrich_df[enrich_df[] < ]
()


img_resp = requests.get(, params={
    : genes, : , : ,
    : , : 
})
 (, )  f:
    f.write(img_resp.content)

Workflow 2: Network Expansion from Seed Proteins

# 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

# 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

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

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

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%.