README.md

metadata

pretty_name: PDB mmCIF Entry Index
license: cc0-1.0
tags:
  - biology
  - proteins
  - protein-structure
  - pdb
  - rcsb
  - mmcif
  - parquet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet
      - split: test
        path: data/test-*.parquet

PDB mmCIF Entry Index

The Protein Data Bank is the single global archive of experimentally-determined 3D structures of biological macromolecules, established in 1971 and now holding well over 230,000 entries. It stores atomic coordinates for proteins, nucleic acids, and their complexes determined by X-ray crystallography, cryo-EM, NMR, micro-electron diffraction, and integrative methods, along with the underlying experimental data (structure factors, EM maps, NMR restraints) and rich metadata covering sequence, ligands, modifications, oligomeric state, and validation reports. Every entry has a four-character PDB ID (e.g. 7PZB) and is distributed primarily in the mmCIF format, with legacy PDB-format files retained for compatibility.Operationally, the archive is jointly managed by the wwPDB consortium: RCSB PDB at Rutgers and UCSD handles deposits from the Americas and Oceania and serves as the wwPDB Archive Keeper, PDBe at EMBL-EBI handles Europe and Africa, PDBj at Osaka University handles Asia, and BMRB hosts NMR-specific data. All wwPDB sites receive synchronized weekly updates and serve the archive free of charge under CC0. Within structural biology and protein ML, the PDB is the canonical training and validation source for structure prediction (AlphaFold2/3, RoseTTAFold, Protenix, OpenFold), inverse folding (ProteinMPNN, ESM-IF), docking, MD setup, and template-based modelling, and time-cutoff splits on PDB release dates are the standard way to control for data leakage when benchmarking these models.

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.

Citation

@article{vallat2026rcsbpdb,
  title     = {{RCSB Protein Data Bank}: Delivering integrative structures alongside experimental structures and computed structure models},
  author    = {Vallat, Brinda and Rose, Yana and Piehl, Dennis W. and Duarte, Jose M. and Bittrich, Sebastian and Bi, Chunxiao and Segura, Joan and Zalevsky, Arthur and Sekharan, Monica R. and Webb, Benjamin M. and others},
  journal   = {Nucleic Acids Research},
  year      = {2026},
  publisher = {Oxford University Press},
  doi       = {10.1093/nar/gkaf1187}
}