InterPro Entries

This dataset contains a viewer-friendly Parquet table derived from the InterPro current release files in this repository. Each row is one InterPro entry from current_release/interpro.xml.gz.

The original source release files remain in the repository. The default datasets configuration uses the normalized Parquet files under data/ so that the Hugging Face Dataset Viewer and load_dataset() can read the entries directly.

Large release artifacts such as current_release/match_complete.xml.gz, current_release/protein2ipr.dat.gz, and current_release/sites.xml.gz are preserved as source files but are not loaded by the default table.

Splits

The split is deterministic by InterPro identifier: sha256(interpro_id) % 10. Bucket 0 is test; buckets 1 through 9 are train.

Split	Rows
train	46,440
test	5,049
total	51,489

Dataset Statistics

Field	Value
InterPro release	108.0
Release date	29th January 2026
Entries	51,489
Member database rows	18
Entries with GO mappings	14,799
InterPro-to-GO mapping rows	30,200
Entries with structures	30,172
Entries with publications	38,194

Entry type	Rows
Family	27,308
Domain	19,276
Homologous_superfamily	3,510
Conserved_site	768
Repeat	395
Active_site	133
Binding_site	82
PTM	17

GO category	Mappings
molecular_function	13,802
biological_process	11,059
cellular_component	5,339

Usage

Install the Hugging Face Datasets library:

pip install datasets

Load all splits:

from datasets import load_dataset

ds = load_dataset("LiteFold/InterPro")
print(ds)

row = ds["train"][0]
print(row["interpro_id"], row["name"], row["entry_type"])

Load one split:

from datasets import load_dataset

train = load_dataset("LiteFold/InterPro", split="train")
test = load_dataset("LiteFold/InterPro", split="test")

Stream rows without downloading the full table first:

from datasets import load_dataset

stream = load_dataset("LiteFold/InterPro", split="train", streaming=True)
for row in stream.take(5):
    print(row["interpro_id"], row["short_name"], row["protein_count"])

Filter entries with GO mappings:

from datasets import load_dataset

ds = load_dataset("LiteFold/InterPro", split="train")
with_go = ds.filter(lambda row: row["go_count"] > 0)
print(with_go[0]["interpro_id"], with_go[0]["go_ids"])

Filter protein domains with PDB structures:

from datasets import load_dataset

ds = load_dataset("LiteFold/InterPro", split="train")
domains_with_structures = ds.filter(
    lambda row: row["entry_type"] == "Domain" and row["structure_count"] > 0
)
print(domains_with_structures[0]["interpro_id"], domains_with_structures[0]["pdb_ids"][:5])

Load release database metadata directly:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="LiteFold/InterPro",
    repo_type="dataset",
    filename="metadata/database_info.parquet",
)
database_info = pd.read_parquet(path)
print(database_info)

Columns

Column	Description
interpro_id	InterPro accession, such as IPR000001.
interpro_numeric_id	Numeric portion of interpro_id.
name	Full InterPro entry name.
short_name	Short InterPro entry name from the XML attribute.
entry_type	Entry class, such as Family, Domain, or Homologous_superfamily.
protein_count	Number of proteins matched by the entry in the release XML.
is_llm	Whether the entry is marked as LLM-generated in the source XML.
is_llm_reviewed	Whether the LLM marker is reviewed in the source XML.
abstract	Normalized text from the entry abstract.
go_ids	GO identifiers mapped to the InterPro entry.
go_terms	GO term names corresponding to go_ids.
go_categories	GO namespaces corresponding to go_ids.
go_count	Number of mapped GO terms.
member_databases	Member databases contributing signatures.
member_accessions	Signature accessions from member databases.
member_names	Signature names from member databases.
member_protein_counts	Protein counts for member signatures.
member_count	Number of member signatures.
external_databases	External resource database names.
external_accessions	External resource accessions.
external_xrefs	Combined external cross-references as DB:ACCESSION.
external_xref_count	Number of external cross-references.
pdb_ids	PDB structure identifiers.
structure_count	Number of PDB structure links.
publication_ids	InterPro publication identifiers.
pubmed_ids	PubMed identifiers, when available.
publication_titles	Publication titles.
publication_years	Publication years, with 0 used when missing.
publication_count	Number of publications attached to the entry.
parent_ids	Parent InterPro entries from the XML hierarchy.
child_ids	Child InterPro entries from the XML hierarchy.
parent_count	Number of parents.
child_count	Number of children.
tree_depth	Minimum hierarchy depth from ParentChildTreeFile.txt, when present.
taxonomy_names	Taxa listed in the taxonomy distribution.
taxonomy_protein_counts	Protein counts for taxonomy_names.
taxonomy_count	Number of taxonomy distribution rows.
key_species_names	Key species names listed for the entry.
key_species_protein_counts	Protein counts for key_species_names.
key_species_count	Number of key species rows.
in_entry_list	Whether the entry appears in entry.list.
entry_list_type	Entry type from entry.list.
entry_list_name	Entry name from entry.list.
names_dat_name	Entry name from names.dat.
short_names_dat_name	Short name from short_names.dat.
split_bucket	Deterministic split bucket from sha256(interpro_id) % 10.

Preparation

The normalization script used to create the Parquet files is included at scripts/prepare_interpro_dataset.py.