InterPro Protein Domain Database
SkillAI & modelsQuery InterPro for protein domains and families. Use when user asks about protein domains, functional sites, protein families, domain architecture, or motifs. Triggers on "interpro", "protein domain", "domain architecture", "protein family", "functional site", "motif".
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the InterPro Protein Domain Database skill
What this skill tells your AI
The instructions your AI receives, as published by biotender-max/awesome-bio-agent-skills in skills/bioclaw/query-interpro/SKILL.md and read by ahel’s review.
Query the InterPro REST API for protein domains, families, and functional sites.
When to Use
- User asks about domains in a protein
- User wants to know what family a protein belongs to
- User asks about functional sites or motifs
- User wants domain architecture visualization
How to Execute
import requests
import json
BASE_URL = "https://www.ebi.ac.uk/interpro/api"
# 1. Get protein annotation (domains/families for a UniProt ID)
def get_protein_domains(uniprot_id):
url = f"{BASE_URL}/protein/uniprot/{uniprot_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 2. Get InterPro entry details
def get_interpro_entry(interpro_id):
url = f"{BASE_URL}/entry/interpro/{interpro_id}"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 3. Search InterPro by text
def search_interpro(query, max_results=10):
url = f"{BASE_URL}/entry/interpro"
params = {"search": query, "page_size": max_results}
r = requests.get(url, params=params, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# 4. Get domain matches for a protein
def get_domain_matches(uniprot_id):
url = f"{BASE_URL}/protein/uniprot/{uniprot_id}/entry/interpro"
r = requests.get(url, headers={"Accept": "application/json"})
r.raise_for_status()
return r.json()
# Example: TP53 domains
domains = get_domain_matches("P04637")
for result in domains.get("results", []):
meta = result.get("metadata", {})
name = meta.get("name", "N/A")
ipr_type = meta.get("type", "N/A")
accession = meta.get("accession", "N/A")
proteins = result.get("proteins", [])
if proteins:
locations = proteins[0].get("entry_protein_locations", [])
for loc in locations:
for frag in loc.get("fragments", []):
start = frag.get("start", "?")
end = frag.get("end", "?")
print(f"{accession} ({ipr_type}): {name} [{start}-{end}]")
Entry Types
domain— Structural/functional domainfamily— Protein familyhomologous_superfamily— Distant homologsrepeat— Repeated motifsite— Active/binding site
Follow-up Suggestions
- "Want me to compare domains across species?"
- "Should I map these domains onto the 3D structure?"
- "Want me to find other proteins with the same domain?"
Signals
- GitHub stars
- 178
- Forks
- 32
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
query-interpro- Source
- github.com/biotender-max/awesome-bio-agent-skills