Datasets:

LiteFold
/

AlphaFoldDB

pretty_name: AlphaFoldDB Prediction Index
license: cc-by-4.0
tags:
  - biology
  - proteins
  - protein-structure
  - alphafold
  - alphafolddb
  - parquet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*.parquet
      - split: test
        path: data/test/*.parquet

AlphaFoldDB Prediction Index

AlphaFoldDB is an open database of predicted protein 3D structures with confidence scores, massively expanding structural coverage for known protein sequences.

Splits

Split	Rows	Parquet files
train	222,017,452	12
test	24,672,064	2
total	246,689,516	14

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

Dataset Statistics

Metric	Value
Rows	246,689,516
Minimum sequence length	5
Approximate median sequence length	278
Mean sequence length	328.55
Maximum sequence length	4,186
Rows without parsed fragment number	5,619,027

Latest-version distribution:

Latest version	Rows
1	5,271,725
2	347,302
6	241,070,489

The mirrored download_metadata.json describes 48 bulk archive files: 16 proteome archives, 30 global-health archives, and 2 Swiss-Prot archives.

Load With `datasets`

from datasets import load_dataset

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

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

Load one split directly:

from datasets import load_dataset

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

Stream rows without materializing the full table locally:

from datasets import load_dataset

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

Construct an AlphaFold DB entry URL from a row:

entry_url = f"https://alphafold.ebi.ac.uk/entry/{row['alphafold_id']}"

Filter to current v6 entries:

from datasets import load_dataset

train = load_dataset("LiteFold/AlphaFoldDB", split="train")
v6_train = train.filter(lambda row: row["latest_version"] == 6)

For large jobs, prefer streaming or process the Parquet files with a columnar engine such as DuckDB, PyArrow, Polars, or Spark.

Columns

Column	Description
`uniprot_accession`	UniProt accession from `accession_ids.csv`.
`alphafold_id`	AlphaFold DB identifier, for example `AF-Q5VSL9-F1`.
`latest_version`	Latest available AlphaFold DB model version for the entry.
`first_residue_index`	First residue index in UniProt numbering.
`last_residue_index`	Last residue index in UniProt numbering.
`sequence_length`	Derived as `last_residue_index - first_residue_index + 1`.
`fragment_number`	Parsed `F<number>` suffix from `alphafold_id`, nullable when the suffix is absent or nonstandard.
`is_fragmented_prediction`	Whether `fragment_number` is greater than 1.
`split_bucket`	Deterministic bucket from `hash(uniprot_accession) % 10`; bucket 0 is test.

Citation

@article{varadi2022alphafolddb,
  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}
}