README.md

---
pretty_name: Protenix Data
size_categories:
  - 100K<n<1M
task_categories:
  - other
tags:
  - biology
  - protein
  - rna
  - structure-prediction
  - tar
  - datasets
configs:
  - config_name: files
    default: true
    data_files:
      - split: train
        path: metadata.csv
  - config_name: shards
    data_files:
      - split: train
        path: shards.csv
---

# Protenix Data

Protenix is ByteDance's open-source PyTorch reproduction of AlphaFold3, a biomolecular structure predictor that handles proteins, DNA, RNA, ligands, ions, and modifications under a unified all-atom diffusion model. Alongside the model code and weights, the team released the full preprocessed training dataset used to train Protenix and its successors, making it one of the largest publicly available AF3-style training corpora.
The released data is built from the wwPDB and is intended to be drop-in usable for AF3-style training. The original v0.5.0 release uses a PDB cutoff of 2021-09-30 (matching AF3), and Protenix-v1-20250630 extends the cutoff to 2025-06-30 on a larger curated set. MSAs are generated with the ColabFold pipeline against UniRef30 and the ColabFold environmental database, with UniRef sequences retained for pairing via taxonomy IDs and the remainder folded into the unpaired MSA features. Protenix-v1 also adds RNA MSAs and HMMER-derived structural templates aligned with the AF3 template strategy.

## Summary

| | |
|---|---:|
| Original files | 931,270 |
| Original payload | 1.05 TiB (1,150,960,949,915 bytes) |
| Tar shards | 52 |
| Archive size | 1.05 TiB (1,152,633,989,120 bytes) |
| Max shard payload | 20.00 GiB |
| Metadata generated | 2026-05-24T16:01:55Z |

## Source Layout

| Directory | Files | Size |
| --- | --- | --- |
| common | 9 | 1.18 GiB |
| indices | 4 | 28.58 MiB |
| mmcif | 220,113 | 282.13 GiB |
| mmcif_bioassembly | 219,912 | 34.89 GiB |
| mmcif_msa_template | 475,724 | 624.02 GiB |
| posebusters_bioassembly | 308 | 41.52 MiB |
| posebusters_mmcif | 429 | 360.26 MiB |
| recentPDB_bioassembly | 8,808 | 1.44 GiB |
| rna_msa | 5,959 | 39.07 GiB |
| search_database | 4 | 88.75 GiB |

## Common File Types

| Extension | Files | Size |
| --- | --- | --- |
| .a3m | 479,867 | 658.57 GiB |
| .cif | 220,543 | 283.34 GiB |
| .fasta | 4 | 88.75 GiB |
| .pkl.gz | 229,028 | 36.37 GiB |
| .csv | 1,816 | 4.53 GiB |
| .pkl | 2 | 255.97 MiB |
| .json | 4 | 58.64 MiB |
| .csv.gz | 1 | 25.70 MiB |
| .txt | 2 | 20.70 MiB |
| .npz | 2 | 435.23 KiB |
| <none> | 1 | 1.50 KiB |

## Shards

| Shard | Files | Payload | Archive |
| --- | --- | --- | --- |
| shards/shard-00000.tar | 3,743 | 19.99 GiB | 20.00 GiB |
| shards/shard-00001.tar | 2,219 | 19.69 GiB | 19.69 GiB |
| shards/shard-00002.tar | 26,895 | 20.00 GiB | 20.04 GiB |
| shards/shard-00003.tar | 17,553 | 20.00 GiB | 20.03 GiB |
| shards/shard-00004.tar | 23,622 | 19.91 GiB | 19.95 GiB |
| shards/shard-00005.tar | 21,546 | 20.00 GiB | 20.03 GiB |
| shards/shard-00006.tar | 18,365 | 20.00 GiB | 20.03 GiB |
| shards/shard-00007.tar | 12,514 | 20.00 GiB | 20.02 GiB |
| shards/shard-00008.tar | 16,486 | 20.00 GiB | 20.03 GiB |
| shards/shard-00009.tar | 14,994 | 20.00 GiB | 20.02 GiB |
| shards/shard-00010.tar | 12,684 | 19.99 GiB | 20.01 GiB |
| shards/shard-00011.tar | 10,438 | 19.97 GiB | 19.99 GiB |
| shards/shard-00012.tar | 13,431 | 20.00 GiB | 20.02 GiB |
| shards/shard-00013.tar | 9,903 | 20.00 GiB | 20.01 GiB |
| shards/shard-00014.tar | 10,788 | 20.00 GiB | 20.02 GiB |
| shards/shard-00015.tar | 9,355 | 20.00 GiB | 20.02 GiB |
| shards/shard-00016.tar | 142,265 | 20.00 GiB | 20.24 GiB |
| shards/shard-00017.tar | 81,080 | 20.00 GiB | 20.13 GiB |
| shards/shard-00018.tar | 15,468 | 20.00 GiB | 20.02 GiB |
| shards/shard-00019.tar | 14,558 | 20.00 GiB | 20.02 GiB |
| shards/shard-00020.tar | 14,807 | 20.00 GiB | 20.02 GiB |
| shards/shard-00021.tar | 13,074 | 20.00 GiB | 20.02 GiB |
| shards/shard-00022.tar | 12,558 | 19.99 GiB | 20.01 GiB |
| shards/shard-00023.tar | 15,179 | 19.99 GiB | 20.02 GiB |
| shards/shard-00024.tar | 13,741 | 20.00 GiB | 20.02 GiB |

Only the first 25 shards are shown here. See `shards.csv` for all 52 shards.

## Metadata

`metadata.csv` columns:

| Column | Description |
|---|---|
| `path` | Original relative path in the source folder. |
| `shard_path` | Tar shard containing the file. |
| `member_path` | Path of the file inside the tar shard. |
| `top_level` | First directory under the source folder. |
| `directory` | Parent directory of the file. |
| `filename` | File basename. |
| `extension` | File extension, preserving compound suffixes such as `.csv.gz`. |
| `size_bytes` | Original file size in bytes. |
| `size_human` | Human-readable original file size. |
| `modified_utc` | Local file modification timestamp captured during packaging. |

`shards.csv` lists one row per tar shard. `_MANIFEST.json` contains the aggregate build summary.

## Download Everything

```bash
pip install -U huggingface_hub
hf download LiteFold/protenix-data --repo-type dataset --local-dir ./protenix-data
```

Extract all shards:

```bash
mkdir -p ./data
for shard in ./protenix-data/shards/*.tar; do
  tar -xf "$shard" -C ./data
done
```

## Use With `datasets`

Use the `datasets` API to query file metadata, then use `huggingface_hub` to download the shard that contains the file.

```python
from datasets import load_dataset
from huggingface_hub import hf_hub_download
import tarfile

files = load_dataset("LiteFold/protenix-data", "files", split="train")
row = files[0]

shard = hf_hub_download(
    repo_id="LiteFold/protenix-data",
    repo_type="dataset",
    filename=row["shard_path"],
)

with tarfile.open(shard) as archive:
    archive.extract(row["member_path"], path="./data")
```

For streaming metadata:

```python
from datasets import load_dataset

files = load_dataset("LiteFold/protenix-data", "files", split="train", streaming=True)
for row in files:
    print(row["path"], row["shard_path"])
    break
```

## Notes

The tar shards are uncompressed by design. This keeps packaging and random extraction simple and avoids spending CPU compressing data that is often already compressed.

# Citation

```
@article{bytedance2025protenix,
  title     = {Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction},
  author    = {ByteDance AML AI4Science Team and Chen, Xinshi and Zhang, Yuxuan and Lu, Chan and Ma, Wenzhi and Guan, Jiaqi and Gong, Chengyue and Yang, Jincai and Zhang, Hanyu and Zhang, Ke and Wu, Shenghao and Zhou, Kuangqi and Yang, Yanping and Liu, Zhenyu and Wang, Lan and Shi, Bo and Shi, Shaochen and Xiao, Wenzhi},
  journal   = {bioRxiv},
  year      = {2025},
  publisher = {Cold Spring Harbor Laboratory},
  doi       = {10.1101/2025.01.08.631967},
  url       = {https://www.biorxiv.org/content/10.1101/2025.01.08.631967v1}
}
```