AlphaFoldDB Prediction Index

This repository contains the AlphaFold Protein Structure Database bulk-download files mirrored under LiteFold/AlphaFoldDB. The added Parquet files make the accession index loadable in the Hugging Face Dataset Viewer and the datasets API.

The default dataset table is built from accession_ids.csv. Each row represents one AlphaFold DB prediction entry and includes its UniProt accession, AlphaFold DB identifier, residue range, latest model version, derived sequence length, parsed fragment number, and deterministic split bucket.

The raw FASTA file and structure tar archives are preserved in the repository but are not embedded in the default Parquet table.

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.

Source Files Used

• accession_ids.csv
• download_metadata.json
• README.txt
• CHANGELOG.txt

The processed files were generated from the raw files already present in this repository. The preparation script is included at scripts/prepare_alphafolddb_dataset.py.