File size: 5,390 Bytes
46a7310 | 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 | ---
license: other
license_name: cc-by-4.0-and-apache-2.0
license_link: LICENSE.md
pretty_name: BioBlobs data
tags:
- biology
- protein
- protein-function
- proteinshake
- venusx
size_categories:
- 100K<n<1M
---
# BioBlobs data
The ProteinShake and VenusX data behind **Unsupervised discovery of Functional Substructures**
(Xin Wang, Kaiwen Shi and Carlos Oliver, NeurIPS 2026), in the layout the BioBlobs code reads.
[Paper](https://arxiv.org/abs/2510.01632) · [Code](https://github.com/OliverLaboratory/BioBlobs) ·
[Model](https://huggingface.co/xwang112358/BioBlobs)
It holds the protein structures, labels and train, validation and test splits. For VenusX it also
holds the paper's ESM2-T30 and SaProt-650M residue embeddings. Build the ProteinShake embeddings
with the code, as described below.
## Contents
| Folder | Task | Structures | Source |
|---|---|---|---|
| `ec_proteinshake/` | EC-L3, enzyme class | 15,603 | ProteinShake, RCSB PDB |
| `pfam_proteinshake/` | Pfam, protein family | 31,109 | ProteinShake, RCSB PDB |
| `go_molecular_function_proteinshake/` | GO-MF, molecular function | 32,633 | ProteinShake, RCSB PDB |
| `scop_fam_proteinshake/` | SCOP-FAM, SCOP family | 10,066 | ProteinShake, RCSB PDB |
| `scop_sf_proteinshake/` | SCOP-SF, SCOP superfamily | 10,066 | ProteinShake, RCSB PDB |
| `venusx_site_fragment/{Act,BindI,Evo,Motif}/MF50/` | VenusX functional sites | 1,743 / 2,038 / 12,776 / 2,368 | VenusX, AlphaFold DB |
Each ProteinShake task has a `sequence` and a `structure` split, which assign clusters of similar
sequences or structures (0.7 similarity) to disjoint splits. VenusX uses its Mixed-Family MF-50
split. SCOP-FAM and SCOP-SF share the same structures, which are included in both folders.
The VenusX embeddings are the ones behind the paper's VenusX results. They cover 1,732 / 2,028 /
12,723 / 2,358 proteins of Act / BindI / Evo / Motif. The other 84 proteins have AlphaFold2
structures with residues past the end of their VenusX site annotation, and the paper's embedding
build skipped them. The code leaves them out of every split, which gives the paper's test sets of
186 / 203 / 1,291 / 249 samples. The embeddings take 33.0 GB: 11.0 GB for ESM2-T30 and 22.0 GB for
SaProt-650M, stored as float16.
```
<task>_proteinshake/
pdb.tar.gz pdb/<pdb_id>.pdb
labels.csv, token_maps.json EC-L3
targets.jsonl, token_map.json Pfam, GO-MF, SCOP-FAM, SCOP-SF
sequence/{train,val,test}_split.csv
structure/{train,val,test}_split.csv
venusx_site_fragment/<target>/MF50/
pdb.tar.gz pdb/<uniprot_id>.pdb
samples.csv one row per protein and InterPro site
{train,val,test}_split.csv
token_maps.json, metadata.json
esm2_static_cache_mmap/ ESM2-T30 embeddings: embeddings.bin, meta.pt
saprot_static_cache_mmap/ SaProt-650M embeddings: embeddings.bin, meta.pt
```
`embeddings.bin` stacks the per-residue embeddings of all proteins. `meta.pt` holds the protein
names and row offsets and loads with `torch.load(..., weights_only=True)`.
The download is 38.2 GB, of which 33.0 GB are the VenusX embeddings. The structures unpack to
23.6 GB. `SHA256SUMS` lists a checksum for every file (`sha256sum -c SHA256SUMS`).
## Usage
Download the repository as the code's `data/` folder and unpack the structures:
```bash
hf download xwang112358/BioBlobs-data --repo-type dataset --local-dir data
# To skip the 33 GB of VenusX embeddings, add --exclude "*_static_cache_mmap/*"
for f in data/*_proteinshake/pdb.tar.gz data/venusx_site_fragment/*/MF50/pdb.tar.gz; do
tar -xzf "$f" -C "$(dirname "$f")"
done
```
Then follow `docs/REPRODUCE.md` in the code repository. The data-preparation scripts find the
unpacked files, skip the download and parse the structures. For ProteinShake, then build the
ESM2-T30 or SaProt-650M residue embeddings with `preprocessing/prepare_plm_cache.py`. SaProt also
needs Foldseek, which reads the PDB files. VenusX training reads the downloaded embeddings, so skip
this step for VenusX.
## Licenses
- ProteinShake files (`*_proteinshake/`): CC-BY-4.0, as released by ProteinShake. ProteinShake
builds these tasks from RCSB PDB structures and their annotations. Its documentation lists the
source of each task.
- VenusX files (`venusx_site_fragment/`): Apache-2.0, as released by VenusX
(`AI4Protein/VenusX_Res_*_MF50` and `AI4Protein/VenusX_*_AlphaFold2_PDB`). The site annotations
come from InterPro, and the structures are AlphaFold DB models (CC-BY-4.0).
- VenusX embeddings (`venusx_site_fragment/*/MF50/*_static_cache_mmap/`): Apache-2.0, like the
VenusX files they are computed from. They are outputs of ESM2-T30 and SaProt-650M, both released
under the MIT License.
## Citation
If you use this data, please cite BioBlobs and the original datasets.
```bibtex
@inproceedings{wang2026bioblobs,
title = {Unsupervised discovery of Functional Substructures},
author = {Wang, Xin and Shi, Kaiwen and Oliver, Carlos},
booktitle = {Advances in Neural Information Processing Systems},
year = {2026}
}
```
Also cite ProteinShake (Kucera et al., NeurIPS 2023 Datasets and Benchmarks), VenusX
(Tan et al., 2025), InterPro and AlphaFold DB. If you use the VenusX embeddings, also cite ESM2
(Lin et al., Science 2023) and SaProt (Su et al., ICLR 2024).
|