AFDB-1.6M — One Representative Structure Per Structural Cluster

A deduplicated subset of AFDB-24M, containing approximately 1.6 million AlphaFold Database predicted protein structures — one per structural cluster.

How This Dataset Was Created

This dataset was derived from AFDB-24M using the following procedure:

1. All ~24 million rows across 12,005 shards were scanned.
2. Rows were grouped by struct_cluster_id (structural cluster representative from AFDB Foldseek clustering).
3. For each unique struct_cluster_id, the single row with the highest global_plddt (global mean pLDDT confidence score) was selected.
4. The selected rows were written into new Parquet shards (2,000 rows each, ZSTD level 12 compression).

This yields approximately 1.6 million entries — one high-confidence representative per 3D structural fold cluster.

Dataset Summary

Property	Value
Source	AFDB-24M
Total entries	~1.6M (one per struct_cluster_id)
Selection criterion	Highest global_plddt per structural cluster
Format	Apache Parquet, ZSTD compressed (level 12)
Splits	train (98%), val (1%), test (1%) — inherited from AFDB-24M

Schema

Each Parquet file contains a flat table with the following columns (same schema as AFDB-24M):

Column	Type	Description
entry_id	string	AFDB entry ID (e.g., AF-A0A1C0V126-F1)
uniprot_accession	string	UniProt accession (e.g., A0A1C0V126)
tax_id	int64	NCBI taxonomy ID
organism_name	string	Scientific name of the organism
global_plddt	float32	Global mean pLDDT confidence score (70–100)
seq_len	int32	Sequence length in residues
seq_cluster_id	string	AFDB50 sequence cluster representative entry ID
struct_cluster_id	string	Structural cluster representative entry ID
split	string	train, val, or test
gcs_uri	string	Original GCS URI
cif_content	string	Complete raw mmCIF file text

Usage

Loading with PyArrow

import pyarrow.parquet as pq

table = pq.read_table("shard_000000.parquet")
print(table.schema)
print(f"{len(table)} rows")

Loading with Pandas

import pandas as pd

df = pd.read_parquet("shard_000000.parquet")
print(df[["entry_id", "organism_name", "global_plddt", "seq_len", "split"]].head())

Parsing Structures with Gemmi

import gemmi

row = table.to_pydict()
cif_text = row["cif_content"][0]
doc = gemmi.cif.read_string(cif_text)
structure = gemmi.make_structure_from_block(doc.sole_block())
model = structure[0]
chain = model[0]
print(f"{len(chain)} residues")

Data Source and License

• AlphaFold Database structures are provided by DeepMind and EMBL-EBI under CC BY 4.0.
• Cluster files are from the Steinegger lab, based on Foldseek clustering of AFDB v4 (Version 3 clusters).

Citation

If you use this dataset, please cite the AlphaFold Database:

@article{varadi2022alphafold,
  title={AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models},
  author={Varadi, Mihaly and Anyango, Stephen and Deshpande, Mandar and others},
  journal={Nucleic Acids Research},
  volume={50},
  number={D1},
  pages={D439--D444},
  year={2022},
  doi={10.1093/nar/gkab1061}
}

And the AFDB cluster resource:

@article{barrio2024clustering,
  title={Clustering predicted structures at the scale of the known protein universe},
  author={Barrio-Hernandez, Inigo and Yeo, Jimin and Jänes, Jürgen and others},
  journal={Nature},
  volume={622},
  pages={637--645},
  year={2023},
  doi={10.1038/s41586-023-06510-w}
}