query-stringdb

name	query-stringdb
description	Query STRING for protein-protein interactions. Use when user asks about protein interactions, interaction networks, binding partners, or interactome. Triggers on "string", "protein interaction", "interaction network", "binding partners", "interactome", "PPI".

STRING Protein Interaction Database

Query the STRING API for protein-protein interaction networks.

When to Use

• User asks about a protein's interaction partners
• User wants to build an interaction network
• User asks about functional associations between genes
• User wants interaction confidence scores

How to Execute

import requests
import json

BASE_URL = "https://version-12-0.string-db.org/api"

# 1. Get interaction partners
def get_interactions(genes, species=9606, score_threshold=400):
    url = f"{BASE_URL}/json/network"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "required_score": score_threshold,
        "caller_identity": "molclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 2. Get functional enrichment
def get_enrichment(genes, species=9606):
    url = f"{BASE_URL}/json/enrichment"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "caller_identity": "molclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 3. Get interaction partners (expand network)
def get_partners(gene, species=9606, limit=10):
    url = f"{BASE_URL}/json/interaction_partners"
    params = {
        "identifiers": gene,
        "species": species,
        "limit": limit,
        "caller_identity": "molclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 4. Download network image
def download_network_image(genes, species=9606, output_path="/workspace/group/network.png"):
    url = f"{BASE_URL}/highres_image/network"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "caller_identity": "molclaw"
    }
    r = requests.get(url, params=params)
    with open(output_path, 'wb') as f:
        f.write(r.content)
    return output_path

# Example
interactions = get_interactions(["BRCA1", "BRCA2", "TP53"])
for i in interactions[:10]:
    print(f"{i['preferredName_A']} <-> {i['preferredName_B']}  score: {i['score']}")
    print(f"  Sources: experimental={i.get('escore',0)}, database={i.get('dscore',0)}, textmining={i.get('tscore',0)}")

Score Thresholds

• 900+ = Highest confidence
• 700+ = High confidence
• 400+ = Medium confidence (default)
• 150+ = Low confidence

Species IDs

Human=9606, Mouse=10090, Rat=10116, Fly=7227, Yeast=4932, E.coli=511145

Follow-up Suggestions

• "Want me to do enrichment analysis on this network?"
• "Should I expand the network to include more partners?"
• "Want me to download the network image?"