PDB mmCIF Entry Index

This repository contains mirrored Protein Data Bank mmCIF files under mmcif/ plus the PDB entries.idx index. The added Parquet files make the mirrored entries loadable in the Hugging Face Dataset Viewer and the datasets API.

The default dataset table has one row per mmCIF file present in this repository. Rows are joined with matching metadata from entries.idx, including classification, accession date, title, source organism, author list, resolution, and experimental method.

Splits

Split	Rows
train	88,873
test	9,951
total	98,824

The split is deterministic: sha256(pdb_id) % 10 == 0 goes to test; buckets 1 through 9 go to train.

Dataset Statistics

Metric	Value
mmCIF files in this repo	98,824
Rows joined to entries.idx metadata	98,824
Full entries.idx rows	252,816
Total mirrored mmCIF compressed size	31.08 GB
Known-resolution rows	93,997
Unknown-resolution rows	4,827
Median known resolution	2.10 A
Mean known resolution	2.33 A

Top experimental methods:

Method	Rows
X-RAY DIFFRACTION	82,380
ELECTRON MICROSCOPY	11,433
SOLUTION NMR	4,707
ELECTRON CRYSTALLOGRAPHY	101
X-RAY DIFFRACTION, NEUTRON DIFFRACTION	50

Top classifications:

Classification	Rows
HYDROLASE	14,117
TRANSFERASE	9,970
OXIDOREDUCTASE	7,743
VIRAL PROTEIN	4,333
MEMBRANE PROTEIN	3,206

Load With datasets

from datasets import load_dataset

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

row = ds["train"][0]
print(row)

Load one split directly:

from datasets import load_dataset

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

Stream rows without materializing the full table locally:

from datasets import load_dataset

streamed = load_dataset("LiteFold/PDB", split="train", streaming=True)
first_row = next(iter(streamed))

Use the mmcif_path column with hf_hub_download to fetch a structure file:

from datasets import load_dataset
from huggingface_hub import hf_hub_download

row = load_dataset("LiteFold/PDB", split="train[0]")[0]
local_path = hf_hub_download(
    repo_id="LiteFold/PDB",
    repo_type="dataset",
    filename=row["mmcif_path"],
)

Filter to X-ray structures with known resolution:

from datasets import load_dataset

train = load_dataset("LiteFold/PDB", split="train")
xray = train.filter(
    lambda row: row["experimental_method"] == "X-RAY DIFFRACTION"
    and not row["resolution_is_unknown"]
)

Columns

Column	Description
pdb_id	Four-character PDB identifier in lowercase.
mmcif_path	Path to the mirrored gzipped mmCIF file in this repository.
mmcif_file_size_bytes	Compressed mmCIF file size from Hugging Face Hub file metadata.
mmcif_blob_id	Hub blob identifier for the mmCIF object.
pdb_url	RCSB PDB structure page URL.
rcsb_download_url	Direct RCSB mmCIF download URL.
classification	PDB header classification.
accession_date	Original entries.idx accession date string.
accession_date_iso	Parsed ISO date when available.
title	Structure title from entries.idx.
source_organism	Source organism field from entries.idx.
authors	Author list from entries.idx.
raw_resolution	Original resolution field from entries.idx.
resolution_angstrom	Numeric resolution in Angstroms, nullable for non-numeric values such as NOT.
resolution_is_unknown	Whether resolution_angstrom is null.
experimental_method	Experimental method field from entries.idx.
has_entries_idx_metadata	Whether the mmCIF file matched a row in entries.idx.
split_bucket	Deterministic hash bucket; bucket 0 is test.

Source Files Used

• entries.idx
• Hub file metadata for paths under mmcif/**/*.cif.gz

The full parsed entries.idx table is also included as metadata/entries_idx.parquet. The preparation script is included at scripts/prepare_pdb_dataset.py.