InterPro Database

Overview

InterPro (https://www.ebi.ac.uk/interpro/) is a comprehensive resource for protein family and domain classification maintained by EMBL-EBI. It integrates predictive signatures from its member databases — including Pfam, PANTHER, PRINTS, ProSite, SMART, NCBIfam, SUPERFAMILY, CDD, Gene3D, and others (see the member-database table below) — into a unified set of entries, providing a single view of functional annotation across UniProtKB. (The legacy TIGRFAMs were absorbed into NCBIfam.)

InterPro classifies proteins into:

• Families: Groups of proteins sharing common ancestry and function
• Domains: Independently folding structural/functional units
• Homologous superfamilies: Structurally similar protein regions
• Repeats: Short tandem sequences
• Sites: Functional sites (active, binding, PTM)

Key resources:

• InterPro website: https://www.ebi.ac.uk/interpro/
• REST API: https://www.ebi.ac.uk/interpro/api/
• API documentation: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/
• Python client: via requests

Scripts

scripts/query_interpro.py — query the InterPro REST API (stdlib only, JSON to stdout):

python scripts/query_interpro.py protein P04637          # InterPro entries for a UniProt protein
python scripts/query_interpro.py entry IPR000719         # entry details
python scripts/query_interpro.py entry-proteins IPR000719 --page-size 25   # proteins with an entry

When to Use This Skill

Use InterPro when:

• Protein function prediction: What function(s) does an uncharacterized protein likely have?
• Domain architecture: What domains make up a protein, and in what order?
• Protein family classification: Which family/superfamily does a protein belong to?
• GO term annotation: Map protein sequences to Gene Ontology terms via InterPro
• Evolutionary analysis: Are two proteins in the same homologous superfamily?
• Structure prediction context: What domains should a new protein structure be compared against?
• Pipeline annotation: Batch-annotate proteomes or novel sequences

Core Capabilities

1. InterPro REST API

Base URL: https://www.ebi.ac.uk/interpro/api/

import requests

BASE_URL = "https://www.ebi.ac.uk/interpro/api"

def interpro_get(endpoint, params=None):
    url = f"{BASE_URL}/{endpoint}"
    headers = {"Accept": "application/json"}
    response = requests.get(url, params=params, headers=headers)
    response.raise_for_status()
    return response.json()

Endpoint direction gotcha (read this first). In the InterPro API the first path segment is the resource type you get back. To list entries for a protein, the protein filter goes last: entry/InterPro/protein/UniProt/{id}/. To list proteins for an entry, the protein resource goes first: protein/UniProt/entry/InterPro/{id}/. The reversed forms (protein/UniProt/{id}/entry/InterPro/, entry/InterPro/{id}/protein/UniProt/) return only a *_url wrapper with no results and count: null — a silent empty, not an error.

2. Look Up a Protein

def get_protein_entries(uniprot_id):
    """Get all InterPro entries that match a UniProt protein."""
    # Resource (entry) first, protein filter last — see direction gotcha above.
    data = interpro_get(f"entry/InterPro/protein/UniProt/{uniprot_id}/")
    return data

# Example: Human p53 (TP53)
result = get_protein_entries("P04637")
entries = result.get("results", [])

for entry in entries:
    meta = entry["metadata"]  # `name` is a string on this grouped endpoint
    print(f"  {meta['accession']} ({meta['type']}): {meta['name']}")
    # e.g., IPR002117 (family): p53 tumour suppressor family
    #       IPR011615 (domain): p53, DNA-binding domain
    #       IPR010991 (domain): p53, tetramerisation domain

3. Get Specific InterPro Entry

def get_entry(interpro_id):
    """Fetch details for an InterPro entry."""
    return interpro_get(f"entry/InterPro/{interpro_id}/")

# Example: Get the InterPro entry the WW domain maps to
ww_entry = get_entry("IPR001202")
# On the single-entry detail endpoint, `name` is a dict {"name", "short"};
# on list/grouped endpoints it is a plain string. Handle both:
name = ww_entry["metadata"]["name"]
print(f"Name: {name['name'] if isinstance(name, dict) else name}")
print(f"Type: {ww_entry['metadata']['type']}")  # -> domain

# Member-database accessions resolve through the same endpoint shape.
# Use lowercase db names (entry/pfam/...); the response carries the
# integrated InterPro accession under metadata['integrated'].
def get_pfam_entry(pfam_id):
    return interpro_get(f"entry/pfam/{pfam_id}/")

pfam = get_pfam_entry("PF00397")  # metadata['integrated'] == 'IPR001202'

4. Search Proteins by InterPro Entry

def get_proteins_for_entry(interpro_id, database="UniProt", page_size=25):
    """Get all proteins annotated with an InterPro entry."""
    params = {"page_size": page_size}
    # Resource (protein) first, entry filter last — see direction gotcha above.
    data = interpro_get(f"protein/{database}/entry/InterPro/{interpro_id}/", params)
    return data

# Example: count proteins carrying the protein kinase domain
kinase_proteins = get_proteins_for_entry("IPR000719")  # Protein kinase domain
print(f"Total proteins: {kinase_proteins['count']}")  # response has count/next/results

5. Domain Architecture

Per-residue match locations live on the entry/protein grouped endpoint, not on the bare protein/UniProt/{id}/ record (that record only carries metadata, with no entries key). Each entry's hits are under results[].proteins[].entry_protein_locations[].fragments[]:

def get_domain_architecture(uniprot_id):
    """Get the domain architecture of a protein with sequence positions."""
    data = interpro_get(f"entry/InterPro/protein/UniProt/{uniprot_id}/")

    arch = []
    for result in data.get("results", []):
        meta = result["metadata"]
        for prot in result.get("proteins", []):
            for loc in prot.get("entry_protein_locations", []):
                for frag in loc.get("fragments", []):
                    arch.append({
                        "accession": meta["accession"],
                        "type": meta["type"],
                        "name": meta["name"],
                        "start": frag["start"],
                        "end": frag["end"],
                    })
    # Order along the sequence to read off the architecture N->C terminus
    arch.sort(key=lambda d: d["start"])
    return arch

# Example: full domain architecture for EGFR, ordered along the sequence
for d in get_domain_architecture("P00533"):
    print(f"  {d['start']:>5}-{d['end']:<5} {d['accession']} ({d['type']}): {d['name']}")

6. GO Term Mapping

The protein record aggregates GO terms from all of its InterPro signatures under metadata.go_terms (already deduplicated), so no per-entry walk is needed:

def get_go_terms_for_protein(uniprot_id):
    """Get GO terms associated with a protein via InterPro."""
    data = interpro_get(f"protein/UniProt/{uniprot_id}/")
    # NB: go_terms can be present but null for proteins with no GO mapping,
    # so coalesce to [] rather than relying on the dict default.
    return data.get("metadata", {}).get("go_terms") or []

# GO terms look like:
# {"identifier": "GO:0004672", "name": "protein kinase activity",
#  "category": {"code": "F", "name": "molecular_function"}}
# category.code is one of F (molecular_function), P (biological_process),
# C (cellular_component).

To attribute GO terms to specific signatures instead of the protein as a whole, read metadata.go_terms on each entry from entry/InterPro/protein/UniProt/{id}/ (each entry carries its own go_terms).

7. Batch Protein Lookup

def batch_lookup_proteins(uniprot_ids, database="UniProt"):
    """Look up multiple proteins and collect their InterPro entries."""
    import time
    results = {}
    for uid in uniprot_ids:
        try:
            data = interpro_get(f"entry/InterPro/protein/{database}/{uid}/")
            entries = data.get("results", [])
            results[uid] = [
                {
                    "accession": e["metadata"]["accession"],
                    "name": e["metadata"]["name"],
                    "type": e["metadata"]["type"]
                }
                for e in entries
            ]
        except Exception as e:
            results[uid] = {"error": str(e)}
        time.sleep(0.3)  # Rate limiting
    return results

# Example
proteins = ["P04637", "P00533", "P38398", "Q9Y6I9"]
domain_info = batch_lookup_proteins(proteins)
for uid, entries in domain_info.items():
    print(f"\n{uid}:")
    for e in entries[:3]:
        print(f"  - {e['accession']} ({e['type']}): {e['name']}")

8. Search by Text or Taxonomy

def search_entries(query, entry_type=None, taxonomy_id=None):
    """Search InterPro entries by text."""
    params = {"search": query, "page_size": 20}
    if entry_type:
        params["type"] = entry_type  # family, domain, homologous_superfamily, etc.

    endpoint = "entry/InterPro/"
    if taxonomy_id:
        # taxonomy is a filter, not the returned resource, so it can follow
        # the entry resource directly (lowercase 'uniprot' in the path).
        endpoint = f"entry/InterPro/taxonomy/uniprot/{taxonomy_id}/"

    return interpro_get(endpoint, params)

# Search for kinase-related entries
kinase_entries = search_entries("kinase", entry_type="domain")

Query Workflows

Workflow 1: Characterize an Unknown Protein

1. Run InterProScan locally or via the web (https://www.ebi.ac.uk/interpro/search/sequence/) to scan a protein sequence
2. Parse results to identify domain architecture
3. Look up each InterPro entry for biological context
4. Get GO terms from associated InterPro entries for functional inference

# After running InterProScan and getting a UniProt ID:
def characterize_protein(uniprot_id):
    """Complete characterization workflow."""

    # 1. Get all annotations
    entries = get_protein_entries(uniprot_id)

    # 2. Group by type
    by_type = {}
    for e in entries.get("results", []):
        t = e["metadata"]["type"]
        by_type.setdefault(t, []).append({
            "accession": e["metadata"]["accession"],
            "name": e["metadata"]["name"]
        })

    # 3. Get GO terms
    go_terms = get_go_terms_for_protein(uniprot_id)

    return {
        "families": by_type.get("family", []),
        "domains": by_type.get("domain", []),
        "superfamilies": by_type.get("homologous_superfamily", []),
        "go_terms": go_terms
    }

Workflow 2: Find All Members of a Protein Family

1. Identify the InterPro family entry ID (e.g., IPR000719 for protein kinases)
2. Query all UniProt proteins annotated with that entry
3. Filter by organism/taxonomy if needed
4. Download FASTA sequences for phylogenetic analysis

Workflow 3: Comparative Domain Analysis

1. Collect proteins of interest (e.g., all paralogs)
2. Get domain architecture for each protein
3. Compare domain compositions and orders
4. Identify domain gain/loss events

API Endpoint Summary

The returned-resource type is whatever comes first in the path; trailing filters narrow it. Reversing the two halves yields a *_url-only wrapper.

Endpoint	Description
/protein/UniProt/{id}/	Full annotation for a protein (incl. metadata.go_terms)
/entry/InterPro/protein/UniProt/{id}/	InterPro entries for a protein (with match locations)
/entry/InterPro/{id}/	Details of an InterPro entry (name is a {name,short} dict)
/entry/pfam/{id}/	Pfam member-db entry details (metadata.integrated = InterPro id)
/protein/UniProt/entry/InterPro/{id}/	Proteins carrying an entry (paginated, with count)
/entry/InterPro/?search=...	Search/list InterPro entries (name is a string here)
/entry/InterPro/taxonomy/uniprot/{tax_id}/	InterPro entries seen in a taxon (paginated)
/structure/PDB/entry/InterPro/{id}/	Structures mapped to an entry

Member Databases

Source-database names as the API returns them (the source_database field / entry/{db}/... path segment) are shown in parentheses where they differ.

Database	Focus
Pfam (pfam)	Protein domains (HMM profiles)
PANTHER (panther)	Protein families and subfamilies
PRINTS (prints)	Protein fingerprints
ProSite patterns (prosite)	Amino acid patterns
ProSite profiles (profile)	Protein profile patterns
SMART (smart)	Mobile signalling/extracellular domains
NCBIfam (ncbifam)	NCBI curated families (absorbed the former TIGRFAMs)
SUPERFAMILY (ssf)	SCOP structural classification
CDD (cdd)	Conserved Domain Database (NCBI)
HAMAP (hamap)	Microbial protein families
Gene3D (cathgene3d)	CATH structural classification
PIRSF (pirsf)	PIR whole-protein families
SFLD (sfld)	Structure-Function Linkage Database (enzymes)
AntiFam (antifam)	Spurious-ORF filter (false-positive removal)

Best Practices

• Use UniProt accession numbers (not gene names) for the most reliable lookups
• Distinguish types: family gives broad classification; domain gives specific structural/functional units
• InterProScan is faster for novel sequences: For sequences not in UniProt, submit to the web service
• Handle pagination: Large result sets require iterating through pages
• Combine with UniProt data: InterPro entries often include links to UniProt, PDB, and GO

Additional Resources

• InterPro website: https://www.ebi.ac.uk/interpro/
• InterProScan (run locally): https://github.com/ebi-pf-team/interproscan
• API documentation: https://github.com/ProteinsWebTeam/interpro7-api/blob/master/docs/
• Pfam: https://www.ebi.ac.uk/interpro/entry/pfam/
• Citation: Paysan-Lafosse T et al. (2023) Nucleic Acids Research. PMID: 36350672