File size: 3,577 Bytes
1f9dba5 d02cdd1 1f9dba5 d02cdd1 1f9dba5 d02cdd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | ---
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`
```python
from datasets import load_dataset
ds = load_dataset("LiteFold/AlphaFoldDB")
print(ds)
row = ds["train"][0]
print(row)
```
Load one split directly:
```python
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:
```python
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:
```python
entry_url = f"https://alphafold.ebi.ac.uk/entry/{row['alphafold_id']}"
```
Filter to current v6 entries:
```python
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}
}
``` |