BioClaw query-interpro
Query 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".
install
source · Clone the upstream repo
git clone https://github.com/Runchuan-BU/BioClaw
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Runchuan-BU/BioClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/container/skills/query-interpro" ~/.claude/skills/runchuan-bu-bioclaw-query-interpro && rm -rf "$T"
manifest:
container/skills/query-interpro/SKILL.mdsource content
InterPro Protein Domain Database
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
— Structural/functional domaindomain
— Protein familyfamily
— Distant homologshomologous_superfamily
— Repeated motifrepeat
— Active/binding sitesite
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?"