Datasets:
File size: 4,242 Bytes
30043ee | 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 | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
tags:
- protein-structure
- alphafold
- structural-biology
size_categories:
- 1M<n<10M
---
# AFDB-1.6M — One Representative Structure Per Structural Cluster
A deduplicated subset of [AFDB-24M](https://huggingface.co/datasets/timodonnell/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](https://huggingface.co/datasets/timodonnell/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](https://huggingface.co/datasets/timodonnell/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
```python
import pyarrow.parquet as pq
table = pq.read_table("shard_000000.parquet")
print(table.schema)
print(f"{len(table)} rows")
```
### Loading with Pandas
```python
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
```python
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](https://creativecommons.org/licenses/by/4.0/).
- **Cluster files** are from the [Steinegger lab](https://afdb-cluster.steineggerlab.workers.dev/), based on Foldseek clustering of AFDB v4 (Version 3 clusters).
### Citation
If you use this dataset, please cite the AlphaFold Database:
```bibtex
@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:
```bibtex
@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}
}
```
|